Combinatorial Chemistry & High Throughput Screening - Volume 14, Issue 10, 2011

In today's interdisciplinary aspects of drug discovery, researchers frequently need to keep in touch with events in fields different from their own. Among various approaches being applied, the recent trend is to increase the speed and efficiency of drug discovery, from hit identification all the way through to the creation of therapeutic candidates. This has led to huge investments by major pharmaceutical companies/academia's in a variety of high-throughput technologies, with emphasis on synthesizing more compounds, identifying targets, screening them faster and delivery systems; all at reduced cost per compound. These high-throughput technologies have the greatest potential to affect drug discovery as part of the ‘closed-loop’ process, in which numerous compounds are selected for synthesis from a ‘virtual library’ of compounds that could be made by parallel chemistry methods. The process is repeated, and improved active compounds are followed up again, using highthroughput computational methods to aid compound selection, in further loops until the desired compound properties have been achieved. The present issue of Combinatorial & High Throughput Screening brings reviews focused on recent application of highthroughput technologies to identify new drugs. Modern drug discovery technologies in lead discovery have been reviewed with special emphasis on the key components underlying the integration between experimental and computational methods in drug design, highlighting progresses, challenges and future directions. There is also a review devoted to state-of-art methodologies dedicated to virtual highthroughput screenings in new lead identification. The lead optimization and new lead design have been illustrated with examples with emphasis on a combination of general and target specific screening protocols. These advanced experimental methods used for HTS at various steps of drug discovery over the years have generated data of the order terabytes. The need to manage this enormous data has led computing scientists to offer Cloud computing tool which has been reviewed with focus on speed, efficiency and cost effectiveness to accelerate drug discovery. One key point in the development of virtual screening is the accuracy of docking simulation and the accuracy may vary depending on what target is being tested and what kind of molecules composes the screening library. Besides, to accelerate the process of design of new drugs with specific desirable physicochemical and/or biological activity profiles, machine learning, computational pattern recognition or statistical modelling algorithms are needed to generate quantitative correlations between molecular structures and chemical properties or biological activities. Such modelling can be undertaken either with the knowledge of the structure of the biological target (Structure-based screening) involved in the activity or even in the absence of any knowledge of the target structure (Ligand-based screening). A comprehensive review based on both the screening techniques describing methodology, applications and limitations has been discussed. This is followed by a review, summarizing a combination approach using ligand based screening (SVM) and receptor based screening (molecular docking) to facilitate the rapid screening of proteasome inhibitors from large compound library. The method appears to be swift and precise enough leading to the identification of novel and potential proteasome inhibitors of β subunit of P. falciparum and can be a good starting point for developing novel antimalarial drugs. As part of a close loop process in drug discovery, the various approaches to foster the integration of virtual screening with target-based HTS have been reviewed by providing several success stories that will benefit the early-stage drug discovery. Besides, synthesis and screening of compounds in highthroughput mode, combinatorial approach have also been used for safe delivery systems. Recent developments in combinatorial syntheses and parallel screening of cationic polymer libraries for the discovery of efficient and safe gene delivery will be reviewed in this issue.

The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets. Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced chnologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges.

Drug discovery continues to be one of the greatest contemporary challenges and rational application of modelling approaches is the first important step to obtain lead compounds, which can be optimised further. Virtual high throughput screening (VHTS) is one of the efficient approaches to obtain lead structures for a given target. Strategic application of different screening filters like pharmacophore mapping, shape-based, ligand-based, molecular similarity etc., in combination with other drug design protocols provide invaluable insights in lead identification and optimization. Screening of large databases using these computational methods provides potential lead compounds, thus triggering a meaningful interplay between computations and experiments. In this review, we present a critical account on the relevance of molecular modelling approaches in general, lead optimization and virtual screening methods in particular for new lead identification. The importance of developing reliable scoring functions for non-bonded interactions has been highlighted, as it is an extremely important measure for the reliability of scoring function. The lead optimization and new lead design has also been illustrated with examples. The importance of employing a combination of general and target specific screening protocols has also been highlighted.

Continued advancements in the area of technology have helped high throughput creening (HTS) evolve from a linear to parallel approach by performing system level screening. Advanced experimental methods used for HTS at various steps of drug discovery (i.e. target identification, target validation, lead identification and lead validation) can generate data of the order of terabytes. As a consequence, there is pressing need to store, manage, mine and analyze this data to identify informational tags. This need is again posing challenges to computer scientists to offer the matching hardware and software infrastructure, while managing the varying degree of desired computational power. Therefore, the potential of “On-Demand Hardware” and “Software as a Service (SAAS)” delivery mechanisms cannot be denied. This on-demand computing, largely referred to as Cloud Computing, is now transforming the drug discovery research. Also, integration of Cloud computing with parallel computing is certainly expanding its footprint in the life sciences community. The speed, efficiency and cost effectiveness have made cloud computing a ‘good to have tool’ for researchers, providing them significant flexibility, allowing them to focus on the ‘what’ of science and not the ‘how’. Once reached to its maturity, Discovery-Cloud would fit best to manage drug discovery and clinical development data, generated using advanced HTS techniques, hence supporting the vision of personalized medicine.

High throughput in silico methods have offered the tantalizing potential to drastically accelerate the drug discovery process. Yet despite significant efforts expended by academia, national labs and industry over the years, many of these methods have not lived up to their initial promise of reducing the time and costs associated with the drug discovery enterprise, a process that can typically take over a decade and cost hundreds of millions of dollars from conception to final approval and marketing of a drug. Nevertheless structure-based modeling has become a mainstay of computational biology and medicinal chemistry, helping to leverage our knowledge of the biological target and the chemistry of protein-ligand interactions. While ligand-based methods utilize the chemistry of molecules that are known to bind to the biological target, structure-based drug design methods rely on knowledge of the three-dimensional structure of the target, as obtained through crystallographic, spectroscopic or bioinformatics techniques. Here we review recent developments in the methodology and applications of structure-based and ligand-based methods and target-based chemogenomics in Virtual High Throughput Screening (VHTS), highlighting some case studies of recent applications, as well as current research in further development of these methods. The limitations of these approaches will also be discussed, to give the reader an indication of what might be expected in years to come.

In the last decade mass screening strategies became the main source of leads in drug discovery settings. Although high throughput (HTS) and virtual screening (VS) realize the same concept the different nature of these lead discovery strategies (experimental vs theoretical) results that they are typically applied separately. The majority of drug leads are still identified by hit-to-lead optimization of screening hits. Structural information on the target as well as on bound ligands, however, make structure-based and ligand-based virtual screening available for the identification of alternative chemical starting points. Although, the two techniques have rarely been used together on the same target, here we review the existing prominent studies on their true integration. Various approaches have been shown to apply the combination of HTS and VS and to better use them in lead generation. Although several attempts on their integration have only been considered at a conceptual level, there are numerous applications underlining its relevance that early-stage pharmaceutical drug research could benefit from a combined approach.

The emergence and spread of Plasmodium falciparum resistance to existing antimalarials emphasize the impelling search for novel drug targets and chemotherapeutic compounds. The ubiquitin-proteasome system plays a major role in overall protein turnover, in eukaryotic cells including plasmodia. 20S β subunit is the catalytic core of this proteolytic machinery, and hence most of the inhibitors developed are being targeted towards this component. Inhibition of the proteasome is established as a promising strategy to develop novel antimalarial drugs. The present study reports identification of novel drug-like 20S proteasome inhibitors with potential activity against the 20S β subunit of P. falciparum using a combination of ligand based (Support Vector Machines) and receptor based (molecular docking) techniques. The robust learning and generalizing capability of Support Vector Machines (SVM) has been exploited to classify proteasome inhibitors and non-inhibitors, targeted towards P. falciparum 20S proteasome. SVM model has been trained using 170 molecular descriptors of 64 inhibitors and 208 putative non-inhibitors of 20S proteasome. The non-linear classifier based on Radial Basis Function (RBF) kernel yielded highest classification accuracy in comparison to the linear classifier. The best classifier had 5-fold Cross-Validation (CV) accuracy of 97% and Area Under Curve (AUC) of 0.99 reflecting good accuracy of the model. The SVM model rapidly classified compounds with potential proteasomal activity. Subsequently, molecular docking studies aided the generation of focused collection of compounds with good binding affinity towards the substrate-binding site of 20S β subunit. The novel drug-like 20S proteasome inhibitors identified in this study can be a good starting point to develop novel antimalarial drugs.

Gene therapy is an attractive treatment option for diseases of genetic origin, including several cancers and cardiovascular diseases. While viruses are effective vectors for delivering exogenous genes to cells, concerns related to insertional mutagenesis, immunogenicity, lack of tropism, decay and high production costs necessitate the discovery of non-viral methods. Significant efforts have been focused on cationic polymers as non-viral alternatives for gene delivery. Recent studies have employed combinatorial syntheses and parallel screening methods for enhancing the efficacy of gene delivery, biocompatibility of the delivery vehicle, and overcoming cellular level barriers as they relate to polymermediated transgene uptake, transport, transcription, and expression. This review summarizes and discusses recent advances in combinatorial syntheses and parallel screening of cationic polymer libraries for the discovery of efficient and safe gene delivery systems.

Full text available.

The section on patent review will be focused in the areas of interest to the readers of CCHTS. The search was conducted using the following key words: combinatorial chemistry, high throughput screening, drug repurposing, chemical library, high content screening, drug discovery and natural products. All patents highlighted here are identified by the patent number issued either by the World Intellectual Property Organization or by a regional patent office. HIGH THROUGHPUT SCREENING US 7972803 B2: Centrosomal proteins and secretion. (Jurczyk, A., Bortell, R., Rossini, A., Doxsey, S., US). The patent is based on the discovery of role of centrosomal proteins in cellular secretion and encompasses methods for identifying candidate modulators of cellular secretion. Centrosomes are cytosolic organelles comprising of two centrioles, surrounded by the pericentriolar material (PCM) comprising of proteins like pericentrin and γ-tubulin, which are required for microtubule nucleation, organization and cell cycle progression. Centrosome has also been implicated in diverse functions like cell polarization, protein localization in certain signaling pathways and cilia/flagella formation. The inventors identified a new role of centrosome in protein secretion in pancreatic islets through its ability to interface with the secretory vesicles. The centrosome proteins were shown to co-localize with insulin granules in insulinoma cell lines and in isolated mouse islets. A siRNA knockdown of centrosome proteins was accompanied by loss of intracellular insulin granules from beta cells as well as hypersecretion of insulin into the media in vitro and in plasma in vivo, thus suggesting a critical role for centrosome proteins in regulation of insulin storage and secretion. The inventors hypothesize that centrosome is a novel drug target for treatment of diseases associated with abnormal secretion such as diabetes, Huntington's disease, Alzheimer's disease etc. The patent includes methods for screening compound libraries to fins modulators of activity and levels of centrosome proteins including reporter assays, ELISAs and subcellular localization changes using fluorescence microscopy. US 7935493 B2: Fragments of fluorescent proteins for protein fragment complementation Assays. (Michnick, S., MacDonald M., Lamerdin, J., US). The invention provides methods for generating fragments of known reporter fluorescent proteins and creating mutant fragments with improved spectral characteristics for use in Protein-Fragment Complementation Assays (PCAs). The methodology has widespread use in establishing assays for drug discovery, target validation, high-throughput/content screening, pathwaymapping, mechanism-of-action studies, biosensors, and diagnostics. The fragments are generated based on detailed structural information for fluorescent proteins and are designed such that the basic chromophore formation and folding of the protein are not affected. The fragmentation is selected in areas that have little or no impact on the two complementing halves to fold and reconstitute active structures. The fragmentation design is explained in detail for the GFP, a protein in which a α-helix threads through the center of a rigid β-barrel and is attached to the chromophore, p-hydroxybenzylideneimidazolinone formed from residues 65-67. Fragmentation is made in β-turns at extreme ends orany one of six regions of amino-acids spanning the β-loops to preserve the barrel structure. The two fragments, F1 and F2 are amplified by PCR or synthesized as oligonucleotides are fused in frame at 5' or at 3' end with genes encoding target proteins, A and B, in two different expression vectors. If proteins A & B interact, F1 and F2 are brought in close proximity and are capable of refolding and reconstituting a functional fluorescent protein. The structural homology between known fluorescent proteins from Aequorea, Anemonia and Anthozoa allowed ease of designing complementing fragments. In addition to generating fragments capable for complementation, the inventors created mutant fragments with specific spectral properties with variable intensities and signal shifts in green, yellow, cyan, orange-red and red region. The complementation of fragments and use of the technology for high throughput screening was demonstrated using a number of fusion proteins that are known to have protein-protein interactions like p53 dimerization, PI3kinase and PKA/PKC. The adaptation of the PCA to high throughput assays was shown for interactions between FK506 binding proteins, FKBP and FRAPS (FKBP-rapamycin-Associated protein). Rapamycin induced complex formation between the two proteins and was visualized by fluorescence microscopy and quantified spectrofluormetrically. The methodology also encompasses using multi-color multiplexing as well as combining fluorescent or luminescent PCA......