BackgroundDrug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence.ResultsIn this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site.ConclusionsSide effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.

Ligand induced fit phenomenon occurring at the ligand binding domain of the liver X receptor beta (LXRbeta) was investigated by means of molecular dynamics. Reliability of a 4-ns trajectory was tested from two distinct LXRbeta crystal complexes 1PQ6B/GW and 1PQ9B/T09 characterized by an open and a closed state of the pocket, respectively. Crossed complexes 1PQ6B/T09 and 1PQ9B/GW were then submitted to the same molecular dynamic conditions, which were able to recover LXRbeta conformations similar to the original crystallography data. Analysis of "open to closed" and "closed to open" conformational transitions pointed out the dynamic role of critical residues lining the ligand binding pocket involved in the local remodeling upon ligand binding (e.g., Phe271, Phe329, Phe340, Arg319, Glu281). Altogether, the present study indicates that the molecular dynamic protocol is a consistent approach for managing LXRbeta-related induced fit process. This protocol could therefore be used for refining ligand docking solutions of a structure-based design strategy.

No abstract