Alan Talevi

Prediction of drug intestinal absorption by new linear and non-linear QSPR

In order to minimize the high attrition rate that usually characterizes drug research and development projects, current medicinal chemists aim to characterize both pharmacological and ADME profiles at the beginning of drug R&D initiatives. Thus, the development of ADME High-Throughput Screening in vitro and in silico ADME models has become an important growing research area. Here we present new linear and non-linear predictive QSPR models to predict the human intestinal absorption rate, which are derived from a medium sized, balanced and diverse training set of organic compounds. The structure-property relationships so obtained involve only 4 molecular descriptors, and display an excellent ratio of number of cases to number of descriptors. Their adjustment of the training set data together with the performance achieved during the internal and external validation procedures are comparable to previously reported modeling efforts.

An Integrated Drug Development Approach Applying Topological Descriptors

We describe the opportunities posed by computer-assisted drug design in the light of two aspects of the current drug discovery scenario: the decline of innovation due to high attrition rates at clinical stage of development and the combinatorial explosion emerging from exponential growth of feasible small molecules and genome and proteome exploration. We present an overview of recent reports from our group in the field of rational drug development, by using topological descriptors (either alone, or in combination with different 3D approaches) and a diversity of modeling techniques such as Linear Discriminant Analysis and the Replacement Method. Modeling efforts aimed at the integrated prediction of several significant molecular properties in the field of drug discovery, such as pharmacological activity, aqueous solubility, human intestinal permeability and affinity to P-glycoprotein (ABCB1, MDR1) are reviewed. The suitability of conformation-independent descriptors to explore large chemical repositories is highlighted, as well as the opportunities posed by in silico guided drug repurposing.

Anticonvulsant activity of artificial sweeteners: A structural link between sweet-taste receptor T1R3 and brain glutamate receptors

A virtual screening campaign based on application of a topological discriminant function capable of identifying novel anticonvulsant agents indicated several widely-used artificial sweeteners as potential anticonvulsant candidates. Acesulfame potassium, cyclamate and saccharin were tested in the Maximal Electroshock Seizure model (mice, ip), showing moderate anticonvulsant activity. We hypothesized a probable structural link between the receptor responsible of sweet taste and anticonvulsant molecular targets. Bioinformatic tools confirmed a highly significant sequence-similarity between taste-related protein T1R3 and several metabotropic glutamate receptors from different species, including glutamate receptors upregulated in epileptogenesis and certain types of epilepsy.

Synthesis of 2-Hydrazolyl-4-Thiazolidinones Based on Multicomponent Reactions and Biological Evaluation Against Trypanosoma Cruzi

A series of 18 novel 2‐hydrazolyl‐4‐thiazolidinones‐5‐carboxylic acids, amides and 5,6‐α,β‐unsaturated esters were synthesized, and their in vitro activity on cruzipain and T. cruzi epimastigotes was determined. Some agents show activity at 37 μm concentration in the enzyme assay. Computational tools and docking were used to correlate the biological response with the physicochemical parameters of the compounds and their cruzipain inhibitory effects.

Computer-guided drug repurposing: Identification of trypanocidal activity of clofazimine, benidipine and saquinavir

In spite of remarkable advances in the knowledge on Trypanosoma cruzi biology, no medications to treat Chagas disease have been approved in the last 40 years and almost 8 million people remain infected. Since the public sector and non-profit organizations play a significant role in the research efforts on Chagas disease, it is important to implement research strategies that promote translation of basic research into the clinical practice. Recent international public-private initiatives address the potential of drug repositioning (i.e. finding second or further medical uses for known-medications) which can substantially improve the success at clinical trials and the innovation in the pharmaceutical field. In this work, we present the computer-aided identification of approved drugs clofazimine, benidipine and saquinavir as potential trypanocidal compounds and test their effects at biochemical as much as cellular level on different parasite stages. According to the obtained results, we discuss biopharmaceutical, toxicological and physiopathological criteria applied to decide to move clofazimine and benidipine into preclinical phase, in an acute model of infection. The article illustrates the potential of computer-guided drug repositioning to integrate and optimize drug discovery and preclinical development; it also proposes rational rules to select which among repositioned candidates should advance to investigational drug status and offers a new insight on clofazimine and benidipine as candidate treatments for Chagas disease. One Sentence Summary: We present the computer-guided drug repositioning of three approved drugs as potential new treatments for Chagas disease, integrating computer-aided drug screening and biochemical, cellular and preclinical tests.

Application of Computer-Aided Drug Repurposing in the Search of New Cruzipain Inhibitors: Discovery of Amiodarone and Bromocriptine Inhibitory Effects

Cruzipain (Cz) is the major cystein protease of the protozoan Trypanosoma cruzi , etiological agent of Chagas disease. From a 163 compound data set, a 2D-classifier capable of identifying Cz inhibitors was obtained and applied in a virtual screening campaign on the DrugBank database, which compiles FDA-approved and investigational drugs. Fifty-four approved drugs were selected as candidates, four of which were acquired and tested on Cz and T. cruzi epimastigotes. Among them, the antiparkinsonian and antidiabetic drug bromocriptine and the antiarrhythmic amiodarone showed dose-dependent inhibition of Cz and antiproliferative activity on the parasite.

Combined Virtual Screening Strategies

The progress in chemical knowledge and synthetic technologies over the last fifty-years has dramatically increased the synthetic accessible chemical entities. Exploration of natural products rich chemodiversity has also expanded the vast chemical universe where medicinal chemist can pursue the identification of new therapeutic agents. Virtual Screening (VS) benefits from computational technology to explore the increasingly vast chemical universe in an efficient manner. The different VS approaches may be characterized by the computational and human time they require, from the highly automated and fast 2D-QSAR ligand-based VS to the more demanding 3D QSAR and target-based (docking) methodologies. Recently, several studies based on the integration of different VS approaches have been proposed, demonstrating that the hit recovery rate may be maintained (or even increased) with a substantial reduction of computing times. Combined virtual screening methodologies usually begin with the least-demanding approaches at the beginning of the VS process and progress to the more accurate, time consuming techniques in the last stages. This review discusses recent 2D/3D QSAR and ligand-based/target-based “synergistic” combinations that allow speeding-up the VS process, permitting accurate and efficient studies on large databases. The impact of the combination of different techniques on the chemical diversity of the compounds retrieved is also discussed.

Application of linear discriminant analysis in the virtual screening of antichagasic drugs through trypanothione reductase inhibition

SummaryWe have performed virtual screening to identify new lead trypanothione reductase inhibitor (TRI) compounds, enzyme present in Tripanozoma cruzi, the agent responsible of Chagas disease. From a training set of 58 compounds, linear discriminant analysis (LDA) was performed using 2D and 3D descriptors as discriminating variables in order to find out which function of descriptors characterizes the active TRI. The values of the statistical parameters F - Snedecor and Wilks λ for the discriminant function (DF) showed good statistical significance, as long as the rate of success in the prediction for both the training and the test set: 91.38% and 88.63%, in that order. Internal validation through the Leave — Group — Out methodology was performed with good results, assuring the stability of the DF. Afterwards, the DF was applied in virtual screening of 422,367 compounds. The optimum range of values of octanol — water partition coefficient for a compound to develop trypanothione reductase inhibition was applied as a second filtering criteria. 739 structurally heterogeneous drugs of the virtual library were selected as promissory TRI.

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.

Several New Diverse Anticonvulsant Agents Discovered in a Virtual Screening Campaign Aimed at Novel Antiepileptic Drugs to Treat Refractory Epilepsy

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.