Automatic filtering and substantiation of drug safety signals.

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.

Integrated decision support for assessing chemical liabilities.

Chemical liabilities, such as adverse effects and toxicity, have a major impact on today's drug discovery process. In silico prediction of chemical liabilities is an important approach which can reduce costs and animal testing by complementing or replacing in vitro and in vivo liability models. There is a lack of integrated, extensible decision support systems for chemical liability assessment which run quickly and have easily interpretable results. Here we present a method which integrates similarity searches, structural alerts, and QSAR models which all are available from the Bioclipse workbench. Emphasis has been placed on interpretation of results, and substructures which are important for predictions are highlighted in the original chemical structures. This allows for interactively changing chemical structures with instant visual feedback and can be used for hypothesis testing of single chemical structures as well as compound collections. The system has a clear separation between methods and data, and the extensible architecture enables straightforward extension via addition of more plugins (such as new data sets and computational models). We demonstrate our method on three important safety end points: mutagenicity, carcinogenicity, and aryl hydrocarbon receptor (AhR) activation. Bioclipse and the decision support implementation are free, open source, and available from http://www.bioclipse.net/decision-support .

Evidence for the hERG Liability of Antihistamines, Antipsychotics, and Anti-Infective Agents: A Systematic Literature Review From the ARITMO Project.

A systematic review was performed to categorize the hERG (human ether-a-go-go-related gene) liability of antihistamines, antipsychotics, and anti-infectives and to compare it with current clinical risk of torsade de pointes (TdP). Eligible studies were hERG assays reporting half-minimal inhibitory concentrations (IC50). A "hERG safety margin" was calculated from the IC50 divided by the peak human plasma concentration (free Cmax ). A margin below 30 defined hERG liability. Each drug was assigned an "uncertainty score" based on volume, consistency, precision, and internal and external validity of evidence. The hERG liability was compared to existing knowledge on TdP risk (www.credibledrugs.org). Of 1828 studies, 82 were eligible, allowing calculation of safety margins for 61 drugs. Thirty-one drugs (51%) had evidence of hERG liability including 6 with no previous mention of TdP risk (eg, desloratadine, lopinavir). Conversely, 16 drugs (26%) had no evidence of hERG liability including 6 with known, or at least conditional or possible, TdP risk (eg, chlorpromazine, sulpiride). The main sources of uncertainty were the validity of the experimental conditions used (antihistamines and antipsychotics) and nonuse of reference compounds (anti-infectives). In summary, hERG liability was categorized for 3 widely used drug classes, incorporating a qualitative assessment of the strength of available evidence. Some concordance with TdP risk was observed, although several drugs had hERG liability without evidence of clinical risk and vice versa. This may be due to gaps in clinical evidence, limitations of hERG/Cmax data, or other patient/drug-specific factors that contribute to real-life TdP risk.