Drug interaction with UDP-Glucuronosyltransferase (UGT) enzymes is a predictor of drug-induced liver injury.

Background And Aims: DILI frequently contributes to the attrition of new drug candidates and is a common cause for the withdrawal of approved drugs from the market. Although some noncytochrome P450 (non-CYP) metabolism enzymes have been implicated in DILI development, their association with DILI outcomes has not been systematically evaluated.

Approach And Results: In this study, we analyzed a large data set comprising 317 drugs and their interactions in vitro with 42 non-CYP enzymes as substrates, inducers, and/or inhibitors retrieved from historical regulatory documents using multivariate logistic regression.

Metabolic-based drug-drug interactions prediction, recent approaches for risk assessment along drug development.

Prediction of in vivo drug-drug interactions (DDIs) from in vitro and in vivo data, also named in vitro in vivo extrapolation (IVIVE), is of interest to scientists involved in the discovery and development of drugs. To avoid detrimental DDIs in humans, new drug candidates should be evaluated for their possible interaction with other drugs as soon as possible, not only as an inhibitor or inducer (perpetrator) but also as a substrate (victim). DDI risk assessment is addressed along the drug development program through an iterative process as the features of the new compound entity are revealed.

Knowledge-based analysis of multi-potent G-protein coupled receptors ligands.

A large number of chemical structures that interact with G-protein coupled receptors (GPCRs) have been disclosed in patents or published papers. Most of these compounds are selective for a given protein target; however, it is well recognized that some GPCR-drugs interact with multiple targets. Using a literature database, we have identified compounds that act on different GPCRs.

Drug repositioning using in silico compound profiling.

Background: Drug repositioning is a current strategy to find new uses for existing drugs, patented or not, and for late-stage candidates that failed for lack of efficacy.

Results: In silico profiling of several marketed drugs (methadone, rapamycin, saquinavir and telmisartan) was performed, exploiting a vast amount of published information. Similar compounds were assessed in terms of target-activity profiles for major drug-target families.

Assessing potency of c-Jun N-terminal kinase 3 (JNK3) inhibitors using 2D molecular descriptors and binary QSAR methodology.

JNK3 signaling pathway is gaining interest due to its involvement in many neurological disorders. The purpose of this study was to explore for the first time the use of a large and diverse dataset in combination with binary QSAR methodology for predicting JNK3 activity class. Data were extracted from Aureus Pharma' AurSCOPE Kinase knowledge database and active or inactive classes were assigned to ligands based on IC50 biological activity.

Development of an ADME and drug-drug interactions knowledge database for the acceleration of drug discovery and development.

It is widely recognised that predicting or determining the absorption, distribution, metabolism and excretion (ADME) properties of a compound as early as possible in the drug discovery process helps to prevent costly late-stage failures. Although in recent years high-throughput in vitro absorption distribution metabolism excretion toxicity (ADMET) screens have been implemented, more efficient in silico filters are still highly needed to predict and model the most relevant metabolic and pharmacokinetic end points, and thereby accelerate drug discovery and development. The usefulness of the data generated and published for the chemist, biologist or project manager who ultimately wants to understand and optimise the ADME properties of lead compounds cannot be argued with.

Recursive partitioning for the prediction of cytochromes P450 2D6 and 1A2 inhibition: importance of the quality of the dataset.

The purpose of this study was to explore the use of detailed biological data in combination with a statistical learning method for predicting the CYP1A2 and CYP2D6 inhibition. Data were extracted from the Aureus-Pharma highly structured databases which contain precise measures and detailed experimental protocol concerning the inhibition of the two cytochromes. The methodology used was Recursive Partitioning, an easy and quick method to implement.