María Esperanza Ruiz

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.

Biopharmaceutical Relevance of Dissolution Profile Comparison: Proposal of a Combined Approach

The aims of the present study were to evaluate the performance of the main methods proposed for the comparison of percentage dissolved versus time curves and to recommend a more biorelevant combined approach for the comparison of dissolution profiles of multisource drug products. In vitro dissolution tests of four brands of oxcarbazepine (OxCBZ) tablets were performed, and the resulting profiles were compared by model-independent, model-dependent, and ANOVA-based statistical methods. After a careful analysis of the results, some methods were chosen and applied to the comparison of dissolution profiles of four brands of carbamazepine (CBZ) tablets and two brands of phenytoin (PHT) capsules. Finally, these in vitro results were qualitatively correlated with the corresponding in vivo results previously obtained with the same CBZ and PHT products assayed in healthy volunteers. The analysis of the dissolution data obtained with OxCBZ tablets allowed discarding the ANOVA-based statistical methods since in all cases they were over-discriminating from a biopharmaceutical point of view. The remaining comparison methods were applied to in vitro profiles of CBZ and PHT products and the results correlated with in vivo data. The most suitable methods for the biopharmaceutical comparison of in vitro dissolution profiles were the model-independent ones, and among them, the best correlations were the f2 similarity factor along with a measure of the dissolution extent (e.g., area under the curve). This combined approach gives a robust and informative result with the most biopharmaceutical relevance.

Current State and Future Perspectives in QSAR Models to Predict Blood Brain Barrier penetration in Central Nervous System Drug R&D.

The Blood-Brain Barrier (BBB) is a physical and biochemical barrier that restricts the entry of certain drugs to the Central Nervous System (CNS), while allowing the passage of others. The ability to predict the permeability of a given molecule through the BBB is a key aspect in CNS drug discovery and development, since neurotherapeutic agents with molecular targets in the CNS should be able to cross the BBB, whereas peripherally acting agents should not, to minimize the risk of CNS adverse effects. In this review we examine and discuss QSAR approaches and current availability of experimental data for the construction of BBB permeability predictive models, focusing on the modeling of the biorelevant parameter unbound partitioning coefficient (Kp,uu). Emphasis is made on two possible strategies to overcome the current limitations of in silico models: considering the prediction of brain penetration as a multifactorial problem, and increasing experimental datasets through accurate and standardized experimental techniques.

Computer-Aided Recognition of ABC Transporters Substrates and Its Application to the Development of New Drugs for Refractory Epilepsy

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.

Hybrid inhalable microparticles for dual controlled release of levofloxacin and DNase: physicochemical characterization and in vivo targeted delivery to the lungs

Current medical treatments against recurrent pulmonary infections caused by Pseudomonas aeruginosa, such as cystic fibrosis (CF) disorder, involve the administration of inhalable antibiotics. The main challenge is, however, the eradication of microbial biofilms immersed in dense mucus that requires high and recurrent antibiotic doses. Accordingly, the development of novel drug delivery systems capable of providing local and controlled drug release in the lungs is a key factor to improve the therapeutic outcome of such therapeutic molecules. Inhalable hybrid carriers were prepared by co-precipitation of CaCO3 in the presence of alginate and the resulting microparticles were treated with alginate lyase (AL) in order to modify their porosity and enhance the drug loading. The hybrid microparticles were loaded with DNase (mucolytic agent) and levofloxacin (LV, wide-spectrum antibiotic) in the range of 20-40% for LV and 28-67% for DNase, depending on the AL treatment. In vitro studies demonstrated that microparticles were able to control the DNase release for 24 h, while 30-50% of LV was released in 3 days. The morphological characterization was performed by optical, fluorescence and scanning electron microscopies, showing a narrow size distribution (5 μm). FTIR, XRD, DSC and nitrogen adsorption isotherm studies revealed the presence of the drugs in a non-crystalline state. A microcidal effect of microparticles was found on P. aeruginosa in agar plates and corroborated by Live/Dead kit and TEM observations. Finally, to study whether the microparticles improved the localization of LV in the lungs, in vivo studies were performed by pulmonary administration of microparticles to healthy mice via nebulization and dry powder inhalation, followed by the quantification of LV in lung tissue. The results showed that microparticles loaded with LV delivered the antibiotic at least 3 times more efficiently than free LV. The developed system opens the gateway to new drug delivery systems that may provide enhanced therapeutic solutions against bacterial infections and in particular as a potential tool in CF pathology.

Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage

Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter linked to the multidrug resistance phenomenon in many diseases such as epilepsy and cancer and a potential source of drug interactions. For these reasons, the early identification of substrates and nonsubstrates of this transporter during the drug discovery stage is of great interest. We have developed a computational nonlinear model ensemble based on conformational independent molecular descriptors using a combined strategy of genetic algorithms, J48 decision tree classifiers, and data fusion. The best model ensemble consists in averaging the ranking of the 12 decision trees that showed the best performance on the training set, which also demonstrated a good performance for the test set. It was experimentally validated using the ex vivo everted rat intestinal sac model. Five anticonvulsant drugs classified as nonsubstrates for BRCP by the model ensemble were experimentally evaluated, and none of them proved to be a BCRP substrate under the experimental conditions used, thus confirming the predictive ability of the model ensemble. The model ensemble reported here is a potentially valuable tool to be used as an in silico ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug-drug interactions.

A derivative UV spectrophotometric method for the determination of levothyroxine sodium in tablets

A derivative UV (D-UV) spectrophotometric method was developed for the determination of Levothyroxine Sodium (L-T4) in tablets of different doses. Quantification was performed using the second derivative of the absorption spectrum at 253 nm (2D253) in methanol: water (50: 50; v/v) (pH 11.2). The method was validated and compared with an HPLC procedure carried out using a RP-18 column (125 × 4 mm, 5 μm) and methanol: phosphoric acid (0.1%) (70: 30, v/v) (pH 3) as mobile phase. Flow rate was set at 1.5 mL/min, and detection was performed at 225 nm. The proposed D-UV method was linear in the range 3.0–40.0 μg/mL with an appropriate precision and accuracy, and it was selective for the drug under study. On the other hand, results obtained by 2D253 analysis were similar to those obtained by HPLC, with no statistically significant differences between them. Therefore, it was concluded that the developed method is suitable for the determination of L-T4 in tablets at the tested doses.

Molecular Topology and Other Promiscuity Determinants as Predictors of Therapeutic Class - A Theoretical Framework to Guide Drug Repositioning?

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.

Carbamazepine-loaded solid lipid nanoparticles and nanostructured lipid carriers: Physicochemical characterization and in vitro/in vivo evaluation

Solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC) represent promising alternatives for drug delivery to the central nervous system. In the present work, four different nanoformulations of the antiepileptic drug Carbamazepine (CBZ) were designed and prepared by the homogenization/ultrasonication method, with encapsulation efficiencies ranging from 82.8 to 93.8%. The formulations remained stable at 4 °C for at least 3 months. Physicochemical and microscopic characterization were performed by photon correlation spectroscopy (PCS), transmission electron microscopy (TEM), atomic force microscopy (AFM); thermal properties by differential scanning calorimetry (DSC), thermogravimetry (TGA) and X-ray diffraction analysis (XRD). The results indicated the presence of spherical shape nanoparticles with a mean particle diameter around 160 nm in a narrow size distribution; the entrapped CBZ displayed an amorphous state. The in vitro release profile of CBZ fitted into a Baker-Lonsdale model for spherical matrices and almost the 100% of the encapsulated drug was released in a controlled manner during the first 24 h. The apparent permeability of CBZ-loaded nanoparticles through a cell monolayer model was similar to that of the free drug. In vivo experiments in a mice model of seizure suggested protection by CBZ-NLC against seizures for at least 2 h after intraperitoneal administration. The developed CBZ-loaded lipid nanocarriers displayed optimal characteristics of size, shape and drug release and possibly represent a promising tool to improve the treatment of refractory epilepsy linked to efflux transporters upregulation.

Systematic Comparison of the Performance of Different 2D and 3D Ligand-Based Virtual Screening Methodologies to Discover Anticonvulsant Drugs

Virtual screening encompasses a wide range of computational approaches aimed at the high-throughput, cost-efficient exploration of chemical libraries or databases to discover new bioactive compounds or novel medical indications of known drugs. Here, we have performed a systematic comparison of the performance of a large number of 2D and 3D ligand-based approaches (2D and 3D similarity, QSAR models, pharmacophoric hypothesis) in a simulated virtual campaign on a chemical library containing 50 known anticonvulsant drugs and 950 decoys with no previous reports of anticonvulsant effect. To perform such comparison, we resorted to Receiver Operating Characteristic curves. We also tested the relative performance of consensus methodologies. Our results indicate that the selective combination of the individual approaches (through voting and ranking combination schemes) significantly outperforms the individual algorithms and/or models. Among the best-performing individual approaches, 2D similarity search based on circular fingerprints and 3D similarity approaches should be highlighted. Combining the results from different query molecules also led to enhanced enrichment.