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.

Despite the introduction of more than 15 third generation antiepileptic drugs to the market from 1990 to the moment, about one third of the epileptic patients still suffer from refractory to intractable epilepsy. Several hypotheses seek to explain the failure of drug treatments to control epilepsy symptoms in such patients. The most studied one proposes that drug resistance might be related with regional overactivity of efflux transporters from the ATP-Binding Cassette (ABC) superfamily at the blood-brain barrier and/or the epileptic foci in the brain. Different strategies have been conceived to address the transporter hypothesis, among them inhibiting or down-regulating the efflux transporters or bypassing them through a diversity of artifices. Here, we review scientific evidence supporting the transporter hypothesis along with its limitations, as well as computer-assisted early recognition of ABC transporter substrates as an interesting strategy to develop novel antiepileptic drugs capable of treating refractory epilepsy linked to ABC transporters overactivity.

Steviol glycosides are natural constituents of Stevia rebaudiana (Bert.) Bert. (Asteraceae) that have recently gained worldwide approval as nonnutritive sweeteners by the Joint Food and Agriculture Organization/World Organization Expert Committee on Food Additives. Cheminformatic tools suggested that the aglycone steviol and several of its phase I metabolites were predicted as potential anticonvulsant agents effective in the seizure animal model maximal electroshock seizure (MES) test. Thus, aqueous infusion from S. rebaudiana was tested in the MES test (mice, intraperitoneal administration), confirming dose-dependent anticonvulsant effect. Afterward, isolated stevioside and rebaudioside A were tested in the MES test, with positive results. Though drug repositioning most often focuses on known therapeutics, this article illustrates the possibilities of this strategy to find new functionalities and therapeutic indications for food constituents and natural products.

From a virtual screening campaign, a number of artificial and natural sweeteners were predicted as potential anticonvulsant agents with protective effects in the seizure animal model Maximal Electroshock Seizure (MES) test. In all cases, the predictions were experimentally confirmed in the aforementioned preclinical seizure model. The article reviews and expands previous reports from our group on anticonvulsant activity of those non-nutritive sweeteners, illustrating the potential of virtual screening approaches to propose new medical uses of food additives. This constitutes a particular case of knowledge-based drug repositioning, which may greatly shorten the development time and investment required to introduce novel medications to the pharmaceutical market. We also briefly overview evidence on possible molecular explanations on the anticonvulsant and proconvulsant effects of different non-nutritive sweeteners. Our analysis -based on Swanson's ABC model- suggests that group I metabotropic glutamate receptors and carbonic anhydrase isoform VII (both proposed or validated molecular targets of antiepileptic drugs) might be involved in the anticonvulsant effect of artificial sweeteners. The first hypothesis is in line with recent advances on development of selective modulators of group I metabotropic glutamate receptors as potential antiepileptic agents.

Much interest has been paid in the last decade on molecular predictors of promiscuity, including molecular weight, log P, molecular complexity, acidity constant and molecular topology, with correlations between promiscuity and those descriptors seemingly being context-dependent. It has been observed that certain therapeutic categories (e.g. mood disorders therapies) display a tendency to include multi-target agents (i.e. selective non-selectivity). Numerous QSAR models based on topological descriptors suggest that the topology of a given drug could be used to infer its therapeutic applications. Here, we have used descriptive statistics to explore the distribution of molecular topology descriptors and other promiscuity predictors across different therapeutic categories. Working with the publicly available ChEMBL database and 14 molecular descriptors, both hierarchical and non-hierchical clustering methods were applied to the descriptors mean values of the therapeutic categories after the refinement of the database (770 drugs grouped into 34 therapeutic categories). On the other hand, another publicly available database (repoDB) was used to retrieve cases of clinically-approved drug repositioning examples that could be classified into the therapeutic categories considered by the aforementioned clusters (111 cases), and the correspondence between the two studies was evaluated. Interestingly, a 3- cluster hierarchical clustering scheme based on only 14 molecular descriptors linked to promiscuity seem to explain up to 82.9% of approved cases of drug repurposing retrieved of repoDB. Therapeutic categories seem to display distinctive molecular patterns, which could be used as a basis for drug screening and drug design campaigns, and to unveil drug repurposing opportunities between particular therapeutic categories.