Current Topics in Medicinal Chemistry - Volume 12, Issue 16, 2012

The search for new therapies against neurodegenerative disorders (NDs) such as Alzheimer (AD) and Parkinson (PD) constitutes a very active area. Although the scientific community has realized great efforts for the study of AD and PD from the most diverse points of view, these diseases remain incurable. Consequently, the design of new and more potent compounds for proteins associated with AD and PD represents nowadays, an objective of major importance. In this sense, the protein known as monoamine oxidase B (MAO-B) constitutes one of the key targets for the search of new drug candidates which could be employed as neuroprotective agents in both anti-AD and anti-PD chemotherapies. The present work is focused on the role of the Quantitative-Structure Activity Relationship (QSAR) analysis and molecular docking (MDock) techniques which have been applied for the discovery of new and promising molecular entities with high inhibitory activity against MAO-B. We also give a brief overview about one of the most potent MAO-B inhibitor drugs: rasagiline. Finally, as contribution to the field, we constructed a QSAR model using artificial neural network (ANN) analysis for the virtual screening of potent MAO-B inhibitors. By realizing a careful inspection of the meaning of the variables in the QSAR-ANN model, new rasagiline bioisosteres were suggested as possible potent MAO-B inhibitors.

Thermolysin (TLN) and other thermolysin-like zinc metalloproteinases (TLPs),are important virulence factors for pathogenesis of bacterial infections by suppressing the innate immune system of the host. Therapeutic inhibition ofTLPs is believed to be a novel strategy inthe development of a new generation antibiotics.In the present study inhibition of TLN and angiotensin I-converting enzyme (ACE) by small peptides were studied by in vitro binding assays and theoretical calculations. The capacity of the peptides to inhibitTLN induced cleavage ofthe transcription factor nuclear factor kappa beta (NF-κB) was studied by electrophoretic mobility shift assays (EMSAs).Nine peptides inhibited ACE with IC50 values in the range 0.48 (IVY) to 1408 (HF) μM, while seven inhibited TLN with IC50 values in the range 0.00034 (IY) to 95640 (FW) μM. Calculations indicated that the peptides occupied the S1' and S2' subsites of ACE, and that IY, LW and IW occupiedthe S1' and S2' subsites, while FW, WL and WV occupiedthe S1 and S1' subsites of TLN. EMSA showed that peptides inhibited TLN induced cleavage of NF-κB. The studied peptides may form as a basis for the design of new compoundstargeting TLN with a potential in the treatment of bacterial infections.

Linear and non-linear QSAR studies have been performed in present investigation with multiple linear regressions (MLR) analysis and Support vector machine (SVM) using different kernels. Three relevant descriptors out of fifteen descriptors calculated are identified as LOGP values, G3e and Rte+. Their relationship with biological activity IC50 have provided structural insights in interpretation and serializing the results into a pragmatic approachable technique. QSAR models obtained show statistical fitness and good predictability. SVM using Gaussian kernel function was found more efficient in prediction of IC50 of training set of thirty small molecules HIV-1 capsid inhibitors. Y-scrambling, PRESS and test set were used as validation parameters. SVM was found superior to training set prediction and internal validations and found inferior to external test set (11 molecules) predictions. Wherein MLR analysis it was vice-versa. Mechanistic interpretation of selected descriptors from both the models actuates further research.

During more than five decades, pyridinium oximes have been developed as therapeutic agents used in the medical treatment of poisoning with organophosphorus compounds. Their mechanism of action is reactivation of acetylcholinesterase (AChE) inhibited by organophosphorus agents. Organophosphorus compounds (OPC) are used as pesticides and developed as warfare nerve agents such as tabun, soman, sarin, VX and others. Exposure to even small amounts of an OPC can be fatal and death is usually caused by respiratory failure resulting from paralysis of the diaphragm and intercostal muscles, depression of the brain respiratory center, bronchospasm, and excessive bronchial secretions. The mechanism of OPC poisoning involves phosphorylation of the serine hydroxyl group at the active site of AChE leading to the inactivation of this essential enzyme, which has an important role in neurotransmission. AChE inhibition results in the accumulation of acetylcholine at cholinergic receptor sites, producing continuous stimulation of cholinergic fibers throughout the central and peripheral nervous systems. Presently, a combination of an antimuscarinic agent, e.g. atropine, AChE reactivator such as one of the standard pyridinium oximes (pralidoxime, trimedoxime, obidoxime, HI-6) and diazepam are used for the treatment of organophosphate poisoning in humans. Despite of enormous efforts devoted to synthesis and development of new pyridinium oximes as potential antidotes against poisoning with OPC, only four compounds have found their application in human medicine so far. However, they differ in their activity in poisoning with warfare nerve agents and pesticides and there is still no universal broad-spectrum oxime capable of protecting against all known OPC. In this article the latest data on structure-activity relationship of pyridinium oximes including their efficacy in treatment of poisoning with organophosphorus compounds are reviewed.

Computational algorithms to explore the conformational space of small molecules are complex and computer demand field in chemoinformatics. In this paper a hybrid algorithm to explore the conformational space of organic molecules is presented. This hybrid algorithm is based in a systematic search approach combined with a Monte Carlo based method in order to obtain an ensemble of low-energy conformations simulating the flexibility of small chemical compounds. The Monte Carlo method uses the Metropolis criterion to accept or reject a conformation through an in-house implementation of the MMFF94s force field to calculate the conformational energy. The parallel design of this algorithm, based on the message passing interface (MPI) paradigm, was implemented. The results showed a performance increase in the terms of speed and efficiency.

A number of neurodegenerative diseases have been associated with potentially neurotoxic alterations in the kynurenine pathway. Due to the potent inhibitory effect of kynurenic acid on glutamate receptor function, the potential use of the elevation of its concentrations in the brain in the protection against excitotoxic injury has earned an ever greater interest. The first strong preclinical achievements of protection in transgenic murine models of chronic neurodegenerative diseases by kynurenergic approaches have recently been published. Despite the remarkable neuroprotection provided by these molecules, the potential risk of interfering with cognitive functions when dealing with molecules capable of impairing glutamatergic and cholinergic transmission should always be considered. This issue is of particular interest in light of the high affinity of kynurenic acid towards the glycine site of NMDA receptors, the antagonism of which is known to recapitulate key behavioral features of schizophrenia. In the past decade, however, a number of other sites of action have been revealed, most of them being possible contributors of either the neuroprotective or the cognitive deteriorating effects of kynurenic acid. This paper reviews the current understanding about how kynurenic acid can influence cognitive functions in experimental animals, and discusses the possibility of exploiting the neuroprotective potential of kynurenic acid without impairing cognitive functions.

It is estimated that by 2050 over 100 million people will be affected by the Parkinson's disease (PD). We propose various computational approaches to screen suitable candidate ligand with anti-Parkinson's activity from phytochemicals. Five different types of dopamine receptors have been identified in the brain, D1–D5. Dopamine receptor D3 was selected as the target receptor. The D3 receptor exists in areas of the brain outside the basal ganglia, such as the limbic system, and thus may play a role in the cognitive and emotional changes noted in Parkinson's disease. A ligand library of 100 molecules with anti-Parkinson's activity was collected from literature survey. Nature is the best combinatorial chemist and possibly has answers to all diseases of mankind. Failure of some synthetic drugs and its side effects have prompted many researches to go back to ancient healing methods which use herbal medicines to give relief. Hence, the candidate ligands with anti-Parkinson's were selected from herbal sources through literature survey. Lipinski rules were applied to screen the suitable molecules for the study, the resulting 88 molecules were energy minimized, and subjected to docking using Autodock Vina. The top eleven molecules were screened according to the docking score generated by Autodock Vina Commercial drug Ropinirole was computed similarly and was compared with the 11 phytochemicals score, the screened molecules were subjected to toxicity analysis and to verify toxic property of phytochemicals. R Programming was applied to remove the bias from the top eleven molecules. Using cluster analysis and Confusion Matrix two phytochemicals were computationally selected namely Rosmarinic acid and Gingkolide A for further studies on the disease Parkinson's.

Bibliometric methods for analyzing and describing research output have been supported internationally by the establishment and operation of organizations such as the Institute for Scientific Information (ISI) or Scimago Ranking Institutions (SRI). This study provides an overview of the research performance of major World countries in the field cytokines, Citometric bead assays and QSAR, the most important journals in which they published their research articles, and the most important academic institutions publishing them. The analysis was based on Thomson Scientific’s Web of Science (WoS), and Scimago group calculated bibliometric indicators of publication activity and actual citation impact. Studying the time period 2005–2010, and shows the visibility of Medicinal Chemistry Bioorganic in this thematic noting that the visibility of a journal must take into account not only the impact factor, but the prestige, popularity and representativeness of the theme that addresses the same making a comprehensive assessment of bibliometric indicators.

Computer-aided drug discovery is a growing frontier in science. It covers different sub areas like chemoinformatics and chemogenomics. Chemogenomics is one of the emerging inter-disciplinary approaches in drug discovery, which combines conventional ligand based approach with biological information of drug targets. The main goal of this review is to check effective application of chemogenomics in understanding interactions between all possible ligands and their potential drug targets at molecular level. Recent studies revealed that increased expression of sFRP1an inhibitor of Wnt signalling pathway, seems to be responsible for Elevated Intracellular Pressure (IOP) in glaucoma patients. Glaucoma is a worldwide spread disease. Here, secreted frizzled-related protein-1 (sFRP1) has been used as a target protein. An important role of sFRP1, an antagonist of Wnt signalling pathway, has been found in regulating IOP. Wnt3a ligand protein and a natural compound from marine source Mycaperoxide H - have been used as ligands. In-silico docking of these ligands with sFRP family implies answers to many intricate queries in drug development field. Using above mentioned ligand-protein model in this study, application of chemogenomics has tried to explore the interaction of active site of proteins with the novel ligands. Henceforth, the present review will focus on predictive in-silico chemogenomic approaches with computer aided drug design could be used in drug design domain in identifying new targets in various diseases, in time and cost effective manner.

The number of neurodegenerative diseases has been increasing in recent years. Many of the drug candidates to be used in the treatment of neurodegenerative diseases present specific 3D structural features. An important protein in this sense is the acetylcholinesterase (AChE), which is the target of many Alzheimer's dementia drugs. Consequently, the prediction of Drug-Protein Interactions (DPIs/nDPIs) between new drug candidates and specific 3D structure and targets is of major importance. To this end, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out a rational DPIs prediction. Unfortunately, many previous QSAR models developed to predict DPIs take into consideration only 2D structural information and codify the activity against only one target. To solve this problem we can develop some 3D multi-target QSAR (3D mt-QSAR) models. In this study, using the 3D MI-DRAGON technique, we have introduced a new predictor for DPIs based on two different well-known software. We have used the MARCH-INSIDE (MI) and DRAGON software to calculate 3D structural parameters for drugs and targets respectively. Both classes of 3D parameters were used as input to train Artificial Neuronal Network (ANN) algorithms using as benchmark dataset the complex network (CN) made up of all DPIs between US FDA approved drugs and their targets. The entire dataset was downloaded from the DrugBank database. The best 3D mt-QSAR predictor found was an ANN of Multi-Layer Perceptron-type (MLP) with profile MLP 37:37-24-1:1. This MLP classifies correctly 274 out of 321 DPIs (Sensitivity = 85.35%) and 1041 out of 1190 nDPIs (Specificity = 87.48%), corresponding to training Accuracy = 87.03%. We have validated the model with external predicting series with Sensitivity = 84.16% (542/644 DPIs; Specificity = 87.51% (2039/2330 nDPIs) and Accuracy = 86.78%. The new CNs of DPIs reconstructed from US FDA can be used to explore large DPI databases in order to discover both new drugs and/or targets. We have carried out some theoretical-experimental studies to illustrate the practical use of 3D MI-DRAGON. First, we have reported the prediction and pharmacological assay of 22 different rasagiline derivatives with possible AChE inhibitory activity. In this work, we have reviewed different computational studies on Drug- Protein models. First, we have reviewed 10 studies on DP computational models. Next, we have reviewed 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compounds to find Drug-Protein QSAR models. Last, we have developped a 3D multi-target QSAR (3D mt-QSAR) models for the prediction of the activity of new compounds against different targets or the discovery of new targets.