No abstract

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.

A virtual screening campaign was conducted in order to discover new anticonvulsant drug candidates for the treatment of refractory epilepsy. To this purpose, a topological discriminant function to identify antiMES drugs and a sequential filtering methodology to discriminate P-glycoprotein substrates and nonsubstrates were jointly applied to ZINC 5 and DrugBank databases. The virtual filters combine an ensemble of 2D classifiers and docking simulations. In the light of the results, 10 structurally diverse compounds were acquired and tested in animal models of seizure and the rotorod test. All 10 candidates showed some level of protection against MES test.

The applications of pharmaceutical and medical nanosystems are among the most intensively investigated fields in nanotechnology. A relevant point to be considered in the design and development of nanovehicles intended for medical use is the formation of the "protein corona" around the nanoparticle, that is, a complex biomolecular layer formed when the nanovehicle is exposed to biological fluids. The chemical nature of the protein corona determines the biological identity of the nanoparticle and influences, among others, the recognition of the nanocarrier by the mononuclear phagocytic system and, thus, its clearance from the blood. Recent works suggest that Surface Plasmon Resonance (SPR), extensively employed for the analysis of biomolecular interactions, can shed light on the formation of the protein corona and its interaction with the surroundings. The synthesis and characterization of solid lipid nanoparticles (SLN) coated with polymers of different chemical nature (e.g., polyvinyl alcohol, chitosans) are reported. The proof-of-concept for the use of SPR technique in characterizing protein-nanoparticle interactions of surface-immobilized proteins (immunoglobulin G and bovine serum albumin, both involved in the formation of the corona) subjected to flowing SLN is demonstrated for non-chitosan-coated nanoparticles. All assayed nanosystems show more preference for IgG than for BSA, such preference being more pronounced in the case of polyvinyl-alcohol-coated SLN.