New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling.

Chemotypes are a new approach for representing molecules, chemical substructures and patterns, reaction rules, and reactions. Chemotypes are capable of integrating types of information beyond what is possible using current representation methods (e.g., SMARTS patterns) or reaction transformations (e.g., SMIRKS, reaction SMILES). Chemotypes are expressed in the XML-based Chemical Subgraphs and Reactions Markup Language (CSRML), and can be encoded not only with connectivity and topology but also with properties of atoms, bonds, electronic systems, or molecules. CSRML has been developed in parallel with a public set of chemotypes, i.e., the ToxPrint chemotypes, which are designed to provide excellent coverage of environmental, regulatory, and commercial-use chemical space, as well as to represent chemical patterns and properties especially relevant to various toxicity concerns. A software application, ChemoTyper has also been developed and made publicly available in order to enable chemotype searching and fingerprinting against a target structure set. The public ChemoTyper houses the ToxPrint chemotype CSRML dictionary, as well as reference implementation so that the query specifications may be adopted by other chemical structure knowledge systems. The full specifications of the XML-based CSRML standard used to express chemotypes are publicly available to facilitate and encourage the exchange of structural knowledge.

Development of a Standardized Rating Tool for Drug Alerts to Reduce Information Overload.

BACKGROUND: A well-known problem in current clinical decision support systems (CDSS) is the high number of alerts, which are often medically incorrect or irrelevant. This may lead to the so-called alert fatigue, an overriding of alerts, including those that are clinically relevant, and underuse of CDSS in general. OBJECTIVES: The aim of our study was to develop and to apply a standardized tool that allows its users to evaluate the quality of system-generated drug alerts. The users' ratings can subsequently be used to derive recommendations for developing a filter function to reduce irrelevant alerts. METHODS: We developed a rating tool for drug alerts and performed a web-based evaluation study that also included a user review of alerts. In this study the following categories were evaluated: "data linked correctly", "medically correct", "action required", "medication change", "critical alert", "information gained" and "show again". For this purpose, 20 anonymized clinical cases were randomly selected and displayed in our customized CDSS research prototype, which used the summary of product characteristics (SPC) for alert generation. All the alerts that were provided were evaluated by 13 physicians. The users' ratings were used to derive a filtering algorithm to reduce overalerting. RESULTS: In total, our CDSS research prototype generated 399 alerts. In 98 % of all alerts, medication data were rated as linked correctly to drug information; in 93 %, the alerts were assessed as "medically correct"; 19.5 % of all alerts were rated as "show again". The interrater-agreement was, on average, 68.4 %. After the application of our filtering algorithm, the rate of alerts that should be shown again decreased to 14.8 %. CONCLUSIONS: The new standardized rating tool supports a standardized feedback of user-perceived clinical relevance of CDSS alerts. Overall, the results indicated that physicians may consider the majority of alerts formally correct but clinically irrelevant and override them. Filtering may help to reduce overalerting and increase the specificity of a CDSS.

The effect object paradigm--a means to support medication safety with clinical decision support.

BACKGROUND: In many countries, officially approved drug information known as summary of product characteristics (SPC) is mostly available in text form, which cannot be used for Clinical Decision Support Systems (CDSS). It may be essential however to substantiate CDSS advice with such legally binding text snippets. In an attempt to link various drug data sources including SPC towards a CDSS to support medication safety in psychiatric patients we arrived at the notion of an effect object. METHODS: A requirements analysis revealed data items and data structure which are needed from the patient and from the drug information source for the CDSS functionality. Published drug data modelling approaches were analyzed and found unsuitable. A conceptional database modeling approach using top down and bottom up modeling was performed. RESULTS: The schema based data model implemented within the django framework centered on SPC "effect objects" which comprise all SPC data required for the respective CDSS function such as search for contraindications in the proposed medication. Today six effect objects have been defined for contraindications and warnings, missing indications, adverse effects, drug-drug interactions, dosing and pharmacokinetics. CONCLUSION: The transformation of SPC data to a database-driven "effect objects" structure permits decoupling between the CDSS functions and different underlying data sources and supports the design of reusable, stable and verified CDSS functions.

Enabling the exploration of biochemical pathways.

The Biochemical Pathways Wall Chart (http://www.expasy.org/tools/pathways/ref.1) has been converted into a molecule and reaction database. Major features of this database are that each molecule is represented by lists of all atoms and bonds (as connection tables), and in the reactions the reaction centre, the atoms and bonds directly involved in the bond rearrangement process, are marked. The information in the database has been enriched by a set of diverse 3D structure conformations generated by the programs CORINA and ROTATE. The web-based structure and reaction retrieval system C@ROL provides a wide range of search methods to mine this rich database. The database is accessible at http://www2.chemie.uni-erlangen.de/services/biopath/index.html and http://www.mol-net.de/databases/biopath.html .