Computational and Synthetic Target-based Approaches to the Discovery of Novel Anticonvulsant Compounds.

BACKGROUND: During the past decades, an important number of anticonvulsant drugs have been incorporated into the collection of drugs to treat epilepsy. However, two main difficulties remain unsolved in therapy: the development of drug-resistant epilepsy and the occurrence of severe toxic effects caused by the medication in responsive patients. The retrospective analysis of the strategies for discovering known anticonvulsant drugs showed that screening campaigns on animal models of epilepsy have been almost the exclusive strategy for identifying the marketed compounds. However, the actual structural and functional information about the molecular targets of the anticonvulsant drugs and the increasing knowledge of the molecular alterations that generate epileptic seizures allow a more rational identification of active compounds. OBJECTIVE: This review compiles target-based strategies used for the discovery of new anticonvulsant candidates and is divided in two main topics. The first one provides an overview of the computational approaches (docking-based virtual screening and molecular dynamics) to find anticonvulsant structures that interact with the voltage-gated ion channels and the enzyme carbonic anhydrase. The second one includes the analysis of active compounds synthesized to act simultaneously on different molecular targets by the combination of pharmacophores of anticonvulsant drugs. CONCLUSION: Current knowledge of the architectures of anticonvulsant targets makes computational simulations attractive methods for the discovery and optimization of active compounds. Combining the results achieved by virtual screening of different targets could lead to multitarget compounds, as an alternative to the design of structures that merge scaffolds of known drugs.

Development of conformation independent computational models for the early recognition of breast cancer resistance protein substrates.

ABC efflux transporters are polyspecific members of the ABC superfamily that, acting as drug and metabolite carriers, provide a biochemical barrier against drug penetration and contribute to detoxification. Their overexpression is linked to multidrug resistance issues in a diversity of diseases. Breast cancer resistance protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting oral absorption and brain bioavailability of its substrates. Early recognition of BCRP substrates is thus essential to optimize oral drug absorption, design of novel therapeutics for central nervous system conditions, and overcome BCRP-mediated cross-resistance issues. We present the development of an ensemble of ligand-based machine learning algorithms for the early recognition of BCRP substrates, from a database of 262 substrates and nonsubstrates compiled from the literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models were developed through application of linear discriminant analysis to random subsamples of Dragon molecular descriptors. Simple data fusion and statistical comparison of partial areas under the curve of ROC curves were applied to obtain the best 2-model combination, which presented 82% and 74.5% of overall accuracy in the training and test set, respectively.