Combinatorial Chemistry & High Throughput Screening - Volume 16, Issue 3, 2013

The half maximal inhibitory concentration (IC50) has several limitations that make it unsuitable for examining a large number of compounds in cytotoxicity studies, particularly when multiple exposure periods are tested. This article proposes a new approach to measure drug effectiveness, which allows ranking compounds according to their toxic effects on live cells. This effectiveness measure, which combines all exposure times tested, compares the growth rates of a particular cell line in the presence of the compound with its growth rate in the presence of DMSO alone. Our approach allows measuring a wider spectrum of toxicity than the IC50 approach, and allows automatic analyses of a large number of compounds. It can be easily implemented in linear regression software, provides a comparable measure of effectiveness for each investigated compound (both toxic and non-toxic), and allows statistically testing the null hypothesis that a compound is non-toxic versus the alternative that it is toxic. Importantly, our approach allows defining an automated decision rule for deciding whether a compound is significantly toxic. As an illustration, we describe the results of a cellbased study of the cytotoxicity of 24 analogs of novobiocin, a C-terminal inhibitor of heat shock protein 90 (Hsp90); the compounds were ranked in order of cytotoxicity to a panel of 18 cancer cell lines and 1 normal cell line. Our approach may also be a good alternative to computing the half maximal effective concentration (EC50) in studies searching for compounds that promote cell growth.

The Lankenau Institute for Medical Research Chemical Genomics Center, Inc. has developed a new (patents issued and pending) Nanotube Automated Repository System (NARS) for dynamic storage of millions of ‘single-shot’ samples stored in a new monolithic microtiter-storage tube plate of our own design we call ‘nanotubes.’ We have integrated the NARS with customized software to efficiently access up to 10,000,000 samples stored continuously frozen (–20°C) in a dehumidified enclosure and sealed in a new microtiter NARS plate that is SBS compliant. Additional software was developed to analyze HTS data from orthogonally pooled compound libraries. Following ‘de-convolution’ of pooled HTS data, the software designates confirmatory retest samples to be ‘cherry-picked’ using the NARS. The application of a new, fully-integrated infrastructure for new leads discovery is described in detail. Other applications for our technologies and new infrastructure are discussed.

Databases and exchange formats describing biological entities such as chemicals and proteins, along with their relationships, are a critical component of research in life sciences disciplines, including chemical biology wherein small information about small molecule properties converges with cellular and molecular biology. Databases for storing biological entities are growing not only in size, but also in type, with many similarities between them and often subtle differences. The data formats available to describe and exchange these entities are numerous as well. In general, each format is optimized for a particular purpose or database, and hence some understanding of these formats is required when choosing one for research purposes. This paper reviews a selection of different databases and data formats with the goal of summarizing their purposes, features, and limitations. Databases are reviewed under the categories of 1) protein interactions, 2) metabolic pathways, 3) chemical interactions, and 4) drug discovery. Representation formats will be discussed according to those describing chemical structures, and those describing genomic/proteomic entities.

Positive allosteric modulators (PAMs) of receptors represent a class of pharmacologic agents having the desirable property of acting only in the presence of cognate ligands. Discovery and optimization of the structure activity relationships of PAMs is complicated by the requirement of a second ligand to manifest their action, and by the need to quantify both affinity and intrinsic efficacy. Multivariate regression analysis is a statistical method capable of simultaneously obtaining affinity and intrinsic efficacy parameters from curve fits of multiple agonist dose-response functions generated in the presence of varying concentrations of PAMs. Capitalizing on the advantages of multivariate regression analysis for PAM optimization requires a theoretical framework and a system that facilitates efficient flow of information from data generation through data analysis, storage, and retrieval. We describe here the experimental design, mathematical model and informatics workflow enabling a multivariate regression approach for rapidly obtaining affinity and intrinsic efficacy values for PAMs in a drug discovery setting.

In this study, we present a model whereby a fragmentation of arsenic hydride in a rectangular dielectric barrier discharge (DBD) atomizer is investigated. The aim is to elucidate the distribution of the intermediates species and generated free analyte atoms along atomizer channel, which is required to decide the optimal position for spectrometric data acquisition. Simulation results indicate that formation of intermediate species and free arsenic atoms is initiated in the first section of atomization channel before reaching the section between the electrodes. Moreover, concentration of free arsenic atoms saturates to a maximum and does not vary thereafter along atomization channel. This result could be attributed to the presence of abundance of hydrogen radicals along atomization channel which limits recombination reactions and ultimately maintains free atom life, which is so useful for analytical purposes. This outcome suggests an approach for radial data acquisition from any position along DBD atomization channel with same sensitivity. Furthermore, this result indicates that DBD atomizer is appropriate for analytical purposes and competitive to other well known atomization tools such as a quartz cell atomizer. The model has been verified experimentally upon examining arsenic and mercury qualitatively from applying chemical vapour generation techniques. Approximately similar results obtained from three radial positions along the atomization channel, whereas a significant increase in signal intensity observed when applying axial viewing by 22 and 40% for arsenic and mercury respectively. Furthermore, a quantitative determination for arsenic is also tried; however, the results were found not useful for model validation due to the hydrogen magnification effect on the recorded spectrum.

The aim of this study was to develop an in silico Quantitative Structure Activity Relationship (QSAR) model capable of predicting partitioning of pesticides into breast milk from their respective chemical structures. A large data set of 190 diverse compounds, including drugs and their active metabolites (87%), and pesticides (13%) with experimentally derived milk/plasma (M/P) ratios taken from the literature, was used to train, test and validate a predictive model. Each compound was encoded with 65 calculated chemical structure descriptors. Sensitivity analysis was then used to select a subset of the descriptors that best describe the transfer of pesticides into breast milk and Artificial neural networks modeling was applied to correlate selected descriptors (inputs) with the M/P ratio (output) in order to develop a predictive QSAR. The developed QSAR model included 26 molecular descriptors related to the molecular size, polarity and hydrogen binding capacity. Together with aromatic rings, these descriptors account for molecule's size and hydrophobic interaction capabilities. The average correlation for the final model (incorporating training, testing, and validation) was 0.85. The developed model provides a useful method for predicting the M/P ratios of pesticides from just a sketch of their respective molecular structures. However, these predictions should only be used to assist in the evaluation of risk in conjunction with an assessment of the infant's response to a given drug/pesticide.

Development of calcium channel blockers is attractive, but has in the past been hampered by lack of high throughput electrophysiological technology. This limitation has been overcome by the implementation of automated patch clamp systems that allow identification of state-dependent compounds, which preferentially target pathologically overactive channels. We recently presented a fluorescence-based high-throughput screen for P/Q-type calcium channels followed by automated electrophysiology. Here, we provide a detailed description of the development of the secondary screen, and show the full analysis of the inactivation kinetics of the recombinant P/Q channel that served as a basis for the automated patch clamp protocol. Increasing the length of pre-depolarization shifted the inactivation to more hyperpolarized potentials. No steadystate inactivation was reached up to pre-depolarization durations of 3 min, while stability of the recordings progressively declined. As a compromise, a 3s pre-depolarization protocol was proposed for functional screening. In order to validate the electrophysiological screening, we compared kinetics and pharmacology of recombinant P/Q-type channels between automated and manual patch clamp measurements. Channel activation was similar under both conditions. By contrast, inactivation occurred at more hyperpolarized potentials in the automated system. Therefore, P/Q-type calcium channel inactivation is sensitive to the applied technological platform and needs to be adjusted when performing automated patch clamp recordings. Our results indicate that a thorough analysis of the inactivation kinetics is mandatory, when establishing an electrophysiological screening protocol for calcium channel blockers. As some data obtained by automated recordings may not be identical to manual patch clamp analysis, we recommend a proper initial validation of the screening assay and – if necessary – a posthoc adjustment of automated patch clamp values. The protocol presented here supports hit-to-lead and lead optimization efforts during the development of novel P/Q-type calcium channel blockers, and may be valuable for the generation of assays in other ion channel programs.

Molecular hybridization approach was used to synthesize substituted 2-(2-(4-aryl oxy benzylidene) hydrazinyl)benzo thiazole derivatives with 2-hydrazinobenzothiazole and 4-(alicycli/aryl/biaryl/heteroaryl oxy)benzaldehyde as new anti-TB agents. The synthesized compounds, when tested against H37Rv strains of Mtb using Resazurin Microtitre Assay (REMA) method, showed promising activity (MIC 1.35-36.50μg/mL). 6-chloro-2-(2-(4- (pyridin-4-yloxy) benzylidene) hydrazinyl) benzo[d]thiazole (10v) gave MIC of 1.35 μg/mL. Thus making it, a potential lead could be developed for further antitubercular studies.