Combinatorial Chemistry & High Throughput Screening - Volume 17, Issue 8, 2014

Let's consider the growing popularity of phenotypic screens within the drug development community. Biochemical assays once dominated the field of high throughput chemical screening, but phenotypic experiments are more adept at modeling real physiological behavior, and can simultaneously mimic drug delivery factors including cell membrane permeability, intracellular localization, and aspects of transport and metabolism. Efficiency and cost of phenotypic screens have improved substantially over the years. For all of these reasons, the technology is well suited to fostering lead optimization. The technology has disadvantages, however, in that data complexity hinders de novo SAR rationalization. For preliminary screens over diverse chemotypes, the simpler biochemical construct affords a good platform for classifying hits according to target-specific modulation, thus facilitating systematic pharmacophore perception. With phenotypic screens, activity trends across different chemotypes may actually reflect modulation of different biochemical targets or via distinct interaction modes. Even within the same chemotype family, pharmacophore perception may be occluded by imprecise partitioning of observed bioactivity measurements between fundamental biochemical modulation versus variation across ADME-like factors. So the question arises: can phenotypic screens ultimately support systematic SAR-driven pharmacophore perception and thus fully supplant biochemical screens? The answer may be computational in nature: given a large, accurate phenotypic data set, one should theoretically be able to sort through the various influences to distill a reliable chemotype-specific SAR that distinguishes targetspecific trends from deliverability issues. This can be achieved via data mining. Unfortunately, many people who realize that data mining is designed for such challenges may be missing background insight that is key to exploiting such methods. Most often overlooked is the fact that excellent calculations will rarely rescue weak data. Grasping pharmacophore effects from a screening study requires data sets that are strategically sensitive to variations in molecular effects that dictate physiology. In order for a given chemical to exert specific biochemical activity, it must have the right solubility profile to be available to those biomolecules that must collectively admit, transport and bind the modulator in order to effect applicable bioactivity. Ligand solubility is determined by chemical substructures. Furthermore, every relevant intermolecular interaction is directly influenced by ligand chemical composition. Thus, if one knows which chemical substructures balance appropriate solubility with the interactions required to reach and bind the biomolecular target, one should be able to predict ligand activity. This is the essence of rational drug design and is, furthermore, the type of insight that careful mining of a well crafted data set should yield. A thorough discourse on 'careful' data mining, would take up much more than the space available to an editorial like this, but of more immediate interest to non-informaticians who design chemical screens would be a quick synopsis of what well crafted data sets might look like. To a significant extent, the data set will depend on whether one is trying to refine one's knowledge of the target specific SAR in a system, or if one is searching de novo for promising new chemotypes with activity toward a given phenotype. Ultimately, the strategy lies in giving the informatician the right spread of data to partition the global bioactivity measurement into contributing factors. In the simpler case where a chemotype family of interest has already been identified, the set of compounds screened should focus on chemicals with the desired core scaffold (or close variants thereof) so that subsequent data analysis will be emphasize SAR within that family and avoid informational contamination from other mechanisms of action. However, it can help to retain a small population of compounds derived from chemical families that likely act via distinct mechanisms; this enables computational differentiation not only between chemical properties that influence whether a given compound is active, but also perception of attributes that push modulators toward one mechanism over another. Secondly, although it may seem counter-intuitive to populate your screen with compounds known to have marginal solubility, membrane permeability or transport efficacy, a data set that includes them will also produce analysis that can distinguish molecular properties that favor target-specific interactions, versus those that amplify bioactivity simply by ensuring greater compound availability to the target. For speculative preliminary studies, systematic screening set selection reflects other considerations. Unlike targeted screening, the useful information from general preliminary screens is enhanced by embracing broad chemical functionality to reduce the number of potentially relevant chemotypes that are overlooked. Preliminary screens should also rigorously eschew compounds with poor ADME properties, since at this early stage there is little benefit in degrading chemotype-specific assessments with inactivity arising from factors other than target-compatibility. Resulting SAR analysis may blend factors that reflect target-specific effects with deliverability, but at least will lay a solid foundation for subsequent targeted lead discovery and refinement studies.

The dissolution of the antihypertensive AT1 antagonist olmesartan in methanol generates in situ a new highly bioactive methyl ether analogue via SN1 mechanism involving an intramolecular proton transfer from carboxyl to hydroxyl group. Theoretical calculations confirmed the thermodynamic control preference of methyl ether versus the antagonistic product methyl ester. Α facile synthetic method for olmesartan methyl ether from olmesartan or olmesartan medoxomil is also described. Interestingly, the introduction of the methyl group to olmesartan did not alter its pharmacological properties. This observation opens new avenues in the synthesis of novel drugs, since hydroxyl and carboxylate groups have an orthogonal relationship in many drugs.

DiHydroOrotate DeHydrogenase [huDHODH] is a therapeutic target for Rheumatoid arthritis [RA]. Leflunomide [A771726] is a widely used synthetic inhibitor against huDHODH. We to find more efficient lead like compounds. A four featured E-Pharmacophore A1D4H6R7 was built based on the inhibitor A771726. This pharmacophore was validated by checking its ability to identify known highly active inhibitors of huDHODH and assigning higher fitness scores to them. A reverse validation was also performed where random 4 featured pharmacophores were built and its efficiency in identifying actives was compared with our E-Pharmacophore. Our Epharmacophore was very efficient, since it passed both validations by picking the known active molecules with high fitness scores. This validated E- pharmacophore was searched against the KEGG phytochemicals subset database. This search resulted in 18 molecules which were subjected to docking with huDHODH. The molecules with docking score greater than that of A771726 were selected. The docking results were further validated using MM/GBSA which gave similar ranking with high binding free energy values. The four molecules 6-Methoxytaxifolin, Rhamnetin, Rhamnazin and Pinoquercetin were taken for explicit 3ns simulation and it was observed that all four molecules had acceptable RMSD values and stable interactions. Thus our study, suggests four phytomolecules that might inhibit huDHODH more efficiently than A771726. Interestingly, some of the obtained hits have already been proven in vitro anti-inflammatory activity which confirms that, the developed E-pharmacophore can be used to identify novel small molecules against inflammatory target, huDHODH.

In our continued quest for novel stable carbenes, silylenes, and germylenes, here we compare and contrast multiplicities and relative stabilities of carbenes affected with four different α-ylides. The latter include carbon, immonium, phosphorus, and sulfur ylides substituted at the alpha positions of carbenes with acyclic, cyclic and cyclicunsaturated structural frameworks. Related thermodynamic data are calculated at B3LYP/6-311++G**//B3LYP/6-31+G* and B3LYP/AUG-cc-pVTZ//B3LYP/6-31+G* levels. Investigations reveal the enlargement of the singlet-triplet energy gaps (ΔΕs-t) in the order of immonium > phosphorus > sulfur > carbon. The observed trend, which is taken as an indication of stability, is thoroughly analyzed by applying appropriate isodesmic reactions which differentiate the substituent effects on each of our singlet or triplet carbene. The effect of unsaturation is also probed in each series of the ylide carbenes. The reactivity of the species is discussed in terms of nucleophilicity and electrophilicity.

Plasmodium falciparum is the most lethal form of the genus Plasmodium which causes malaria, a ‘disease of antiquity’. Globally it affects the health and socio-economic development of a large population especially in Sub-Saharan Africa and Southeast Asia. The Plasmodium falciparum dihydrofolate reductase-thymidylate synthase (PfDHFR-TS) is an important target of antimalarial drugs. Mutations at the active site of PfDHFR have resulted in decrease drug binding affinity of DHFR-inhibitors. In the present study we selected ten compounds of Brucea mollis Wall. Ex kurz and checked for their drug likeness using various computational tools and potential interactions with PfDHFR by molecular docking study. Soulameanone, a quassinoid of Brucea mollis Wall. Ex kurz showed better binding affinity when compared to pyrimethamine for both wild and quadruple mutant drug resistant PfDHFR. In addition, similar isomers of soulameanone were screened for their drug likeness and to study their interactions with PfDHFR. Twenty three compounds showed better binding affinity compared to soulameanone.

Many large-scale applications of the organophosphate pesticide malathion have led to widespread environmental contamination. Concentrations are found in the environment well above those which are harmful to humans and environmental organisms. No current method of detection for this pesticide is rapid, cost-effective, and specific for malathion. Therefore, we utilized a stringent Systematic Evolution of Ligands by Exponential Enrichment (SELEX) process to identify a Molecular Recognition Element (MRE) for malathion. This MRE was identified from a large ssDNA library and has an equilibrium dissociation constant (Kd) in the low-nanomolar range. Additionally, it has significant selectivity for malathion in comparison to various other pesticides and metabolites of malathion, which were used as negative targets of selection. The high affinity and selectivity of the ssDNA MRE for malathion is a product of the stringent SELEX selection scheme and will be useful for rapid, inexpensive, and specific detection of malathion in the environment.

1,3-dipolar cycloaddition procedure is one of the most widely practiced methods in order to synthesize heterocyclic compounds. Although, it seems very simple, but, there are numerous precursors of heterocyclic molecules who have more than one positions to react with a 1,3–dipole species. As a result, while using a precursor with more than one position for reaction, it is probable to synthesize several products with different structures. This paper studies all possible interactions of vinyl acetylene, which has two positions for reaction, with methyl azide. This reaction could lead to the emergence of any 1,3-dipolar cycloaddition products. Our ultimate goal is to help researchers to find out how precursors containing both carbon-carbon double, and the triple bonds interact with 1,3- dipolar species. The present study used the DFT calculations at B3LYP/6-311++G(3df,pd) level to check all probable interactions between vinyl acetylene and methyl azide, and determined Potential Energy Surface, and optimized all species.

In order to find a thriving quantitative structure-activity relationship for antitrypanosomal activities (against Trypanosma brucei rhodesiense) of polyphenols that belong to different structural groups, multiple linear regression (MLR) and artificial neural networks (ANN) were employed. The analysis was performed on two different-sized training sets (59% and 78% molecules in the training set), resulting in relatively successful MLR and ANN models for the data set containing the smaller training set. The best MLR model obtained using the five descriptors (R3m+, GAP, DISPv, HATS2m, JGI2) was able to account only for 74% of the variance of antitrypanosomal activities of the training set and achieved a high internal, but low external prediction. Nonlinearities of the best ANN model compared with the linear model improved the coefficient of determination to 98.6%, and showed a better external predictive ability. The obtained models displayed relevance of the distance between oxygen atoms in molecules of polyphenols, as well as stability of molecules, measured by the difference between the energy of the highest occupied molecular orbital and the energy of the lowest unoccupied molecular orbital (GAP) for their activity.

A 2, 2`azino-bis-(3-ethyl-benzothiazoline-6-sulphonate) radical cation-based method was optimized in 96 well plates to evaluate the comparative scavenging potential of phenolic and non-phenolic group of molecules with respect to control. The interactions of these molecules with ABTS radical cation were quantified on the basis of their relative influence on the bleaching of a bluish-green color complex in a structure- and dose-dependent manner. Experimentally, the developed assay provided evidence that phenolic molecules are more reactive with ABTS radical than non-phenolic compounds because of their resonance and hyper-conjugation effects.

Forty five extracts fraction of nine selected Indian medicinal plants, based on their use in traditional systems of medicine were analyzed for their antioxidant potential. All the extracts were investigated for phenol content value calculated in Gallic acid equivalents (% of GAE) and antioxidant potential. Moreover, total phenolic content (% dw equivalents to gallic acid) of all plant extracts were found in the range of 3.04 to 24.03, which correlated with antioxidant activity. The findings indicated a promising antioxidant activity of crude extracts fractions of three plants (Justicia adhatoda, Capparis aphylla and Aegle marmelos) and required the further exploration for their effective utilization. Results indicated that petroleum ether fraction of J. adhatoda out of three plants also possesses the admirable antioxidant abilities with high total phenolic content. Following, in vitro antioxidant activity-guided phytochemical separation procedures, twelve fractions of petroleum ether extract of J. adhatoda were isolated by silica gel column chromatography. One fraction (Rf value: 0.725) showed the noticeable antioxidant activity with ascorbic acid standard in hydroxyl radical scavenging assays. The molecular structures elucidations of purified antioxidant compound were carried out using spectroscopic studies (1H NMR, 13C NMR and MS). This compound was reported from this species for the first time. The results imply that the J. adhatoda might be a potential source of natural antioxidants and 2,6,10,14,18,22-Tetracosahexaene, 2,6,10,15,19,23-hexamethyl is an antioxidant ingredient in J. adhatoda.