Luis E. Bruno-Blanch

New QSPR study for the prediction of aqueous solubility of drug-like compounds

Solubility has become one of the key physicochemical screens at early stages of the drug development process. Solubility prediction through Quantitative Structure-Property Relationships (QSPR) modeling is a growing area of modern pharmaceutical research, being compatible with both High Throughput Screening technologies and limited compound availability characteristic of early stages of drug development. We resort to the QSPR theory for analyzing the aqueous solubility exhibited by 145 diverse drug-like organic compounds (0.781 being the average Tanimoto distances between all possible pairs of compounds in the training set). An accurate and generally applicable model is derived, consisting on a linear regression equation that involves three DRAGON molecular descriptors selected from more than a thousand available. Alternatively, we apply the linear QSPR to other 21 commonly employed validation compounds, leading to solubility estimations that compare fairly well with the performance achieved by previously reported Group Contribution Methods.

Topological virtual screening: a way to find new anticonvulsant drugs from chemical diversity.

A topological virtual screening (tvs) test is presented, which is capable of identifying new drug leaders with anticonvulsant activity. Molecular structures of both anticonvulsant-active and non active compounds, extracted from the Merck Index database, were represented using topological indexes. By means of the application of a linear discriminant analysis to both sets of structures, a topological anticonvulsant model (tam) was obtained, which defines a connectivity function. On the basis of this model, 41 new structures with anticonvulsant activity have been identified by a topological virtual screening.

Pharmacophore searching and QSAR analysis in the design of anticonvulsant drugs

Abstract A set a antiepileptic drugs (AED) that are active against the MES test, including structures that are already in clinical use as well as the new compounds recently designed in our lab, is analyzed in this article in relation to the requirements associated with the manifested activity. The strategy involves the identification of the pharmacophoric pattern, by means of a similarity analysis where the structural and electronic characteristics are compared, followed by a QSAR study. The latter shows the importance of the electronic descriptors, and points to the carbonyl group as the most relevant portion for the definition of the activity. Quantum chemical derived descriptors give the best answer for both the similarity analysis and the statistical fitting.

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.

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.

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.

Synthesis and Anticonvulsant Activity of Amino Acid-Derived Sulfamides

Sulfamides are promising functions for the design of new antiepileptic drugs ( Bioorg. Med. Chem. 2007, 15, 1556-1567; 5604-5614 ). Following previous research in this line, a set of amino acid-derived sulfamides has been designed, synthesized, and tested as new anticonvulsant compounds. The experimental data confirmed the ability of some of the structures to suppress the convulsions originated by the electrical seizure (MES test) at low doses (100 mg/kg).