Diabetes mellitus, a chronic condition caused by defects in insulin secretion, or action, or both, is a group of metabolic disorders, complications of which can contribute significantly to ill health, disability, poor quality of life and premature death. From the three main types of diabetes, Type 2 is by far the most common, accounting for about 90% of cases worldwide. Studies on the role of protein tyrosine phosphatase 1B (PTP1B) have clearly shown that it serves as a key negative regulator of insulin signaling and is involved in the insulin resistance associated with Type 2 diabetes. The present work aims to survey information related to PTP1B research published in the last decade. Emphasis is laid particularly on Quantitative Structure-Activity Relationships (QSAR) based studies that supported so far the design of new, potent and selective PTP1B inhibitors. Finally, the challenges and perspectives of QSAR studies in this field are discussed to show how these method can be used to design new chemical entities with enhanced PTP1B inhibition activity.

Motivation. Meningoencephalitis, caused by the virulent fungus Cryptococcus neoformans, is an important cause of mortality in case of immunocompromised individuals, and new antifungal agents are required to treat such infections. Coumarins have antimeningoencephalitic activity, and here we report our attempt to find out the structural features required for more active congeners. Method. In vitro antifungal activity of coumarins, expressed as MIC50 values (μg/mL) was considered as the biological activity parameter. QSAR study of the data set of coumarins was performed using different parameters, namely physicochemical, topological, geometrical, constitutional, and semiemperical quantum chemical descriptors as well as whole molecular descriptors. Multiple regression analyses were performed to develop QSAR models. Results. The QSAR study highlights the atomic features and molecular descriptors, information content descriptors, topological and constitutional descriptors that affect the antifungal activity of these coumarin analogs. © 2009 BioChem Press.

Blood Serum Proteome-Mass Spectra (SP-MS) may allow detecting Proteome-Early Drug Induced Cardiac Toxicity Relationships (called here Pro-EDICToRs).However, due to the thousands of proteins in the SP, a more realistic alternative representsthe identification of general Pro-EDICToRs patterns instead of a single protein marker. Inthis sense, we introduced a novel Cartesian 2D spectrum graph for SP-MS. Next, wecalculated the graph node-overlapping parameters (nopk) to numerically characterize SPMSby using them as inputs for a Quantitative Proteome-Toxicity Relationship (QPTR)classifier for Pro-EDICToRs with accuracy higher than 80%. This QPTR approach is theresult of adapting the classic blood proteome Quantitative Property-Structure Relationshipmodels (QSPR) used in Chemometrics to low-mass molecules study. Principal ComponentAnalysis (PCA) on the QPTR nopk values explains with one factor (F1) the 82.7% ofvariance. These nopk values were used to construct for the first time a Pro-EDICToRsComplex Network having samples as nodes linked by similarity between two samplesedges. We compared the topology of two sub-networks for the cardiac toxicity and controlsamples and found extreme relative differences for the re-linking (P) and Zagreb (M2)indices (9.5 and 54.2 % respectively) out of 11 parameters. We also compared the subnetworkswith the well-known ideal random networks including Barabasi-Albert,Kleinberg Small World, Erdos-Renyi, and Epsstein Power Law models. Finally, weproposed Partial Order (PO) schemes of the 115 samples based on LDA-probabilities, F1-scores and/or network node degrees. PCA-CN and LDA-PCA based POs with Tanimoto's.

The discovery of new and more potent anti-cancer agents constitutes one of the most active fields of research in chemotherapy. Colorectal cancer (CRC) is one of the most studied cancers because of its high prevalence and number of deaths. In the current pharmaceutical design of more efficient anti-CRC drugs, the use of methodologies based on Chemoinformatics has played a decisive role, including Quantitative-Structure-Activity Relationship (QSAR) techniques. However, until now, there is no methodology able to predict anti-CRC activity of compounds against more than one CRC cell line, which should constitute the principal goal. In an attempt to overcome this problem we develop here the first multi-target (mt) approach for the virtual screening and rational in silico discovery of anti-CRC agents against ten cell lines. Here, two mt-QSAR classification models were constructed using a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted from the molecules and their contributions to anti-CRC activity were calculated using mt-QSAR-LDA model. Several fragments were identified as potential substructural features responsible for the anti-CRC activity and new molecules designed from those fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-CRC agents.

Adenosine receptors (ARs) are widespread on virtually every human organ/tissue, and have long been considered promising therapeutic targets in a wide range of conditions, ranging from cerebral diseases to cancer, including inflammatory disorders. The knowledge acquired up to date in relation to ARs, in particular regarding the molecular biology of the A(3) AR has provided a solid basis that led to the proposal of this receptor as a novel therapeutic target enabling the rational design and development of potent and selective A(3) AR ligands. This review attempts to summarize the most recent developments in the A(3) research field, focusing in particular on Quantitative Structure-Activity Relationships (QSAR) based studies that supported so far the design of new, potent and selective human A(3) AR antagonists. In addition, a classical QSAR modeling study carried out on two series of pyrazolo-triazolopyrimidine derivatives is presented as a case study. Specifically, a systematic evaluation of linear and non-linear models along with a variety of structure representations and feature selection tools is reported. The combination of these techniques (neural networks to capture non-linear relationships in the data and feature selection to prevent over-fitting) was found to produce QSAR models with good overall accuracy and robustness, as well as predictivity on external data. Moreover, the study indicated that the antagonist activity of these derivatives is largely explained by electrostatic, steric and hydrogen-bonding factors, highlighting the role of the size, shape and type of inhibitor in forming effective blocking of the A(3) AR subtype. The developed QSAR models could then be usefully employed to design new compounds selectively active towards the A(3) adenosine receptor.

Alzheimer's disease (AD), a degenerative disease affecting the brain, is the single most common source of dementia in adults. The cause and the progression of AD still remains a mystery among medical experts. As a result, a cure has not yet been discovered, even after decade's worth of research that started since 1906, when the disease was first identified. Despite the efforts of the scientific community, several of the biological receptors associated with AD have not been sufficiently studied to date, limiting in turn the design of new and more potent anti-AD agents. Thus, the search for new drug candidates as inhibitors of different targets associated with AD constitutes an essential part towards the discovery of new and more efficient anti-AD therapies. The present work is focused on the role of the Ligand-Based Drug Design (LBDD) methodologies which have been applied for the elucidation of new molecular entities with high inhibitory activity against targets related with AD. Particular emphasis is given also to the current state of fragment-based ligand approaches as alternatives of the Fragment-Based Drug Discovery (FBDD) methodologies. Finally, several guidelines are offered to show how the use of fragment-based descriptors can be determinant for the design of multi-target inhibitors of proteins associated with AD.

The etiology and clinical treatment of capsular contracture remain unresolved as the causes may be multifactorial. Triamcinolone acetonide applied in the pocket during surgery was reported to be ineffective in prevention of capsular contracture. However, if injected 4-6 weeks after surgery or as a treatment for capsular contracture, decreased applanation tonometry measurements and pain were observed. It was assumed that intraoperative application of triamcinolone was not effective because its effect does not last long enough. However, betadine, antibiotics, and fibrin were found to be effective in preventing capsular contracture with intraoperative applications and are more effective in the early phases of wound healing than in later stages. The role of triamcinolone acetonide in capsule formation is unknown. The purpose of this study was to determine if triamcinolone acetonide modulates breast capsule formation or capsular contracture in the early phases of wound healing in a rabbit model. Rabbits (n = 19) were implanted with one tissue expander and two breast implants and were killed at 4 weeks. Implant pocket groups were (1) Control (n = 10) and (2) Triamcinolone (n = 9). Pressure/volume curves and histological, immunological, and microbiological evaluations were performed. Operating room air samples and contact skin samples were collected for microbiological evaluation. In the triamcinolone group, a decreased capsular thickness, mild and mononuclear inflammation, and negative or mild angiogenesis were observed. There were no significant differences in intracapsular pressure, fusiform cell density, connective tissue, organization of collagen fibers, and microbiological results between the groups. There was no significant difference in the dialysate levels of IL-8 and TNF-alpha, but correlation between IL-8 and TNF-alpha was observed. Triamcinolone acetonide during breast implantation influences early capsule formation and may reduce capsular contracture. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors at www.springer.com/00266.

Periodic density functional theory (DFT) calculations have been used to unravel the mechanism of the NO reduction by CO (NO + CO -> N-2 + CO2) on clean and hydrogen covered gold based catalysts. The effects caused by the presence of low-coordinated atoms on the catalyst were taken into consideration by using the stepped Au(321) surface. A careful analysis of several reaction mechanisms was made and it is concluded that if hydrogen species are not available on the catalyst surface, the N-O bond cleavage will proceed through the ON2O and N2O intermediates while CO reacts directly with formed oxygen adatoms. If hydrogen species are available on the catalyst, the reaction will occur via the NOH and N2O intermediates. However, the reaction has to compete with a more favorable route, where NH3 instead of N-2 is obtained after formation of NOH and NHxOH intermediates. The calculations agree also with the experimental observation of the NCO intermediate species which is formed without energy cost from combination of CO and N fragments. The NCO species is very probably a spectator at moderate temperatures since its evolution toward N-2 and CO2 is less favorable than other possible routes studied in this work. Finally, calculated reaction rate constants at three different temperatures show that most of the reactions studied are only possible at moderately high temperature.