Adverse Drug Reaction Prediction Using Scores Produced by Large-Scale Drug-Protein Target Docking on High-Performance Computing Machines

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60–0.69 and 0.61–0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.     

A chemical–genetic interaction map of small molecules using high‐throughput imaging in cancer cells

Small molecules often affect multiple targets, elicit off‐target effects, and induce genotype‐specific responses. Chemical genetics, the mapping of the genotype dependence of a small molecule's effects across a broad spectrum of phenotypes can identify novel mechanisms of action. It can also reveal unanticipated effects and could thereby reduce high attrition rates of small molecule development pipelines. Here, we used high‐content screening and image analysis to measure effects of 1,280 pharmacologically active compounds on complex phenotypes in isogenic cancer cell lines which harbor activating or inactivating mutations in key oncogenic signaling pathways. Using multiparametric chemical–genetic interaction analysis, we observed phenotypic gene–drug interactions for more than 193 compounds, with many affecting phenotypes other than cell growth. We created a resource termed the Pharmacogenetic Phenome Compendium (PGPC), which enables exploration of drug mode of action, detection of potential off‐target effects, and the generation of hypotheses on drug combinations and synergism. For example, we demonstrate that MEK inhibitors amplify the viability effect of the clinically used anti‐alcoholism drug disulfiram and show that the EGFR inhibitor tyrphostin AG555 has off‐target activity on the proteasome. Taken together, this study demonstrates how combining multiparametric phenotyping in different genetic backgrounds can be used to predict additional mechanisms of action and to reposition clinically used drugs.     

The use of transgenic systems in pharmaceutical research.

Those pharmaceutical companies whose goal is to generate novel innovative drugs are faced with the challenge that only a fraction of the compounds tested in clinical trials eventually become a registered drug. This problem of attrition is compounded by the fact that the clinical trial or development stage is by far the most costly phase of bringing a new drug to market, consuming around 80 per cent of the total spend. Transgenic technology represents an attractive approach to reducing the attrition rate of compounds entering clinical trials by increasing the quality of the target and compound combinations making the transition from discovery into development. Transgenic technology can impact at many points in the discovery process, including target identification and target validation, and provides models designed to alert researchers early to potential problems with drug metabolism and toxicity, as well as providing better models for human diseases. In target identification, transgenic animals harbouring large DNA fragments can be used to narrow down genetic regions. Genetic studies often result in the identification of large genomic regions and one way to decrease the region size is to do complementation studies in transgenic animals using, for example, inserts from bacterial artificial chromosome (BAC) clones. In target validation, transgenic animals can be used for in vivo validation of a specific target. Considerable efforts are being made to establish new, rapid and robust tools with general utility for in vivo validation, but, so far, only transgenic animals work reliably on a wide range of targets. Transgenic animals can also be used to generate better disease models. Predictive animal models to test new compounds and targets will significantly speed up the drug discovery process and, more importantly, increase the quality of the compounds taken further in the research and development process. Humanised transgenic animals harbouring the human target molecule can be used to understand the effect of a compound acting on the human target in vivo. Also, models mimicking human drug metabolism will provide a means of assessing the effect of human-specific metabolites and of understanding the pharmacokinetic properties of potential drugs. In toxicology studies, transgenic animals are providing more predictive models. A good example of this are those models routinely used to look for carcinogenicity associated with new compounds.     

基于酵母水平的高通量药物筛选

随着后基因组时代的到来,越来越多的药物靶标蛋白将会被发现,基于靶标蛋白设计出的化合物也将大量涌现,高通量药物筛选日趋重要。酵母基因组的易操作性及其简单稳定的培养条件,使得该真核微生物成为一种理想的药物筛选工程细胞。本文讨论了选择酵母系统进行细胞水平筛选的优缺点,并从基于靶点和表型两种筛选模式对酵母水平的高通量药物筛选做一总结。     

Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity

Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects.     

A novel genomics approach for the identification of drug targets in pathogens, with special reference to Pseudomonas aeruginosa

Complete genome sequences of several pathogenic bacteria have been determined, and many more such projects are currently under way. While these data potentially contain all the determinants of host-pathogen interactions and possible drug targets, computational tools for selecting suitable candidates for further experimental analyses are currently limited. Detection of bacterial genes that are non-homologous to human genes, and are essential for the survival of the pathogen represents a promising means of identifying novel drug targets. We have used three-way genome comparisons to identify essential genes from Pseudomonas aeruginosa. Our approach identified 306 essential genes that may be considered as potential drug targets. The resultant analyses are in good agreement with the results of systematic gene deletion experiments. This approach enables rapid potential drug target identification, thereby greatly facilitating the search for new antibiotics. These results underscore the utility of large genomic databases for in silico systematic drug target identification in the post-genomic era.     

The Development and Application of the Glucose Uptake Stimulating Cell Model

The plants are great resources for digging leading compounds, and many current anti diabetic drugs are derived from plants. The key to discover compounds with anti diabetic activities from plants relies on the application of anti diabetic drug screening models. In order to establish a more stable and reliable anti diabetic drug screening model, we optimized the screening model based on the glucose uptake of adipocytes. In the previous models, insulin was used as the only positive control, while in our model both insulin and rosiglitazone were used as positive controls, which made the model more stable and reliable. Furthermore, we expanded the application of the model to screen the insulin signaling pathway inhibitors, and Akt1/2 inhibitor which was an inhibitor of insulin signaling pathway was used as positive control. In the end, we screened 16 compounds isolated from plants using this model and identified three active compounds with glucose uptake stimulating activities. We also performed the dose response experiments of compound X15 and X16. Both showed significant dose responses. These activities were first reported at the cell level, providing fundamental data for their mechanisms study of the activities and for the potential development of the drugs in future.     

Displacement of protein-bound aptamers with small molecules screened by fluorescence polarization

Small molecule inhibitors of proteins are invaluable tools in research and as starting points for drug development. However, their screening can be tedious, as most screening methods have to be tailored to the corresponding drug target. Here, we describe a detailed protocol for a modular and generally applicable assay for the identification of small organic compounds that displace an aptamer complexed to its target protein. The method relies on fluorescence-labeled aptamers and the increase of fluorescence polarization upon their binding to the target protein. The assay has high Z'-factors, making it compatible with high-throughput screening. It allows easy automation, making fluorescence readout the time-limiting step. As aptamers can be generated for virtually any protein target, the assay allows identification of small molecule inhibitors for targets or individual protein domains for which no functional screen is available. We provide the step-by-step protocol to screen for antagonists of the cytohesin class of small guanosine exchange factors.     

Yeast as a drug discovery platform in Huntington's and Parkinson's diseases

The high degree of conservation of cellular and molecular processes between the budding yeast Saccharomyces cerevisiae and higher eukaryotes have made it a valuable system for numerous studies of the basic mechanisms behind devastating illnesses such as cancer, infectious disease, and neurodegenerative disorders. Several studies in yeast have already contributed to our basic understanding of cellular dysfunction in both Huntington's and Parkinson's disease. Functional genomics approaches currently being undertaken in yeast may lead to novel insights into the genes and pathways that modulate neuronal cell dysfunction and death in these diseases. In addition, the budding yeast constitutes a valuable system for identification of new drug targets, both via target-based and non-target-based drug screening. Importantly, yeast can be used as a cellular platform to analyze the cellular effects of candidate compounds, which is critical for the development of effective therapeutics. While the molecular mechanisms that underlie neurodegeneration will ultimately have to be tested in neuronal and animal models, there are several distinct advantages to using simple model organisms to elucidate fundamental aspects of protein aggregation, amyloid toxicity, and cellular dysfunction. Here, we review recent studies that have shown that amyloid formation by disease-causing proteins and many of the resulting cellular deficits can be faithfully recapitulated in yeast. In addition, we discuss new yeast-based techniques for screening candidate therapeutic compounds for Huntington's and Parkinson's diseases.     

A mouse model-based screening platform for the identification of immune activating compounds such as natural products for novel cancer immunotherapies

The therapy of cancer continues to be a challenge aggravated by the evolution of resistance against current medications. As an alternative for the traditional tripartite treatment options of surgery, radiation and chemotherapy, immunotherapy is gaining increasing attention due to the opportunity of more targeted approaches. Promising targets are antigen-presenting cells which drive innate and adaptive immune responses. The discovery and emergence of new drugs and lead structures can be inspired by natural products which comprise many highly bioactive molecules. The development of new drugs based on natural products is hampered by the current lack of guidelines for screening these structures for immune activating compounds. In this work, we describe a phenotypic preclinical screening pipeline for first-line identification of promising natural products using the mouse as a model system. Favorable compounds are defined to be non-toxic to immune target cells, to show direct anti-tumor effects and to be immunostimulatory at the same time. The presented screening pipeline constitutes a useful tool and aims to integrate immune activation in experimental approaches early on in drug discovery. It supports the selection of natural products for later chemical optimization, direct application in in vivo mouse models and clinical trials and promotes the emergence of new innovative drugs for cancer treatment.     

High-Content,Image-Based Screening for Drug Targets in Yeast

Background

Drug discovery and development are predicated on elucidation of the potential mechanisms of action and cellular targets of candidate chemical compounds. Recent advances in high-content imaging techniques allow simultaneous analysis of a range of cellular events. In this study, we propose a novel strategy to identify drug targets by combining genetic screening and high-content imaging in yeast.

Methodology

In this approach, we infer the cellular functions affected by candidate drugs by comparing morphologic changes induced by the compounds with the phenotypes of yeast mutants.

Conclusions

Using this method and four well-characterized reagents, we successfully identified previously known target genes of the compounds as well as other genes involved with functionally related cellular pathways. This is the first demonstration of a genetic high-content assay that can be used to identify drug targets based on morphologic phenotypes of a reference mutant panel.     

Fragment-based activity space: smaller is better

Fragment-based drug discovery has the potential to supersede traditional high throughput screening based drug discovery for molecular targets amenable to structure determination. This is because the chemical diversity coverage is better accomplished by a fragment collection of reasonable size than by larger HTS collections. Furthermore, fragments have the potential to be efficient target binders with higher probability than more elaborated drug-like compounds. The selection of the fragment screening technique is driven by sensitivity and throughput considerations, and we advocate in the present article the use of high concentration bioassays in conjunction with NMR-based hit confirmation. Subsequent ligand X-ray structure determination of the fragment ligand in complex with the target protein by co-crystallisation or crystal soaking can focus on confirmed binders.     

Proteomic methods for drug target discovery

The field of drug target discovery is currently very popular with a great potential for advancing biomedical research and chemical genomics. Innovative strategies have been developed to aid the process of target identification, either by elucidating the primary mechanism-of-action of a drug, by understanding side effects involving unanticipated 'off-target' interactions, or by finding new potential therapeutic value for an established drug. Several promising proteomic methods have been introduced for directly isolating and identifying the protein targets of interest that are bound by active small molecules or for visualizing enzyme activities affected by drug treatment. Significant progress has been made in this rapidly advancing field, speeding the clinical validation of drug candidates and the discovery of the novel targets for lead compounds developed using cell-based phenotypic screens. Using these proteomic methods, further insight into drug activity and toxicity can be ascertained.     

Pharmacophore feature prediction and molecular docking approach to identify novel anti‐HCV protease inhibitors

Discovering a potential drug for HCV treatment is a challenging task in the field of drug research. This study initiates with computational screening and modeling of promising ligand molecules. The foremost modeling method involves the identification of novel compound and its molecular interaction based on pharmacophore features. A total of 197 HCV compounds for NS3/4A protein target were screened for our study. The pharmacophore models were generated using PHASE module implemented in Schrodinger suite. The pharmacophore features include one hydrogen bond acceptor, one hydrogen bond donor, and three hydrophobic sites. As a result, based on mentioned hypothesis the model ADHHH.159 corresponds to the CID 59533233. Furthermore, docking was performed using maestro for all the 197 compounds. Among these, the CID 59533313 and 59533233 possess the best binding energy of ?11.75 and ?10.40 kcal/mol, respectively. The interactions studies indicated that the CID complexed with the NS3/4A protein possess better binding affinity with the other compounds. Further the compounds were subjected to calculate the ADME properties. Therefore, it can be concluded that these two compounds could be a potential alternative drug for the development of HCV.     

High-throughput assays for promiscuous inhibitors

High-throughput screening (HTS) searches large libraries of chemical compounds for those that can modulate the activity of a particular biological target; it is the dominant technique used in early-stage drug discovery. A key problem in HTS is the prevalence of nonspecific or 'promiscuous' inhibitors. These molecules have peculiar properties, act on unrelated targets and can dominate the results from screening campaigns. Several explanations have been proposed to account for promiscuous inhibitors, including chemical reactivity, interference in assay read-out, high molecular flexibility and hydrophobicity. The diversity of these models reflects the apparently unrelated molecules whose behaviors they seek to explain. However, a single mechanism may explain the effects of many promiscuous inhibitors: some organic molecules form large colloid-like aggregates that sequester and thereby inhibit enzymes. Hits from HTS, leads for drug discovery and even several drugs appear to act through this mechanism at micromolar concentrations. Here, we report two rapid assays for detecting promiscuous aggregates that we tested against 1,030 'drug-like' molecules. The results from these assays were used to test two preliminary computational models of this phenomenon and as benchmarks to develop new models.     

A method for identification of inhibitors of the phosphorylation reactions of bacterial response regulator proteins using (31)P nuclear magnetic resonance spectroscopy.

Bacterial response regulators are attractive targets for antibacterial drug development, yet random screening against these targets has failed as yet to identify chemicals that constitute viable leads. Alternative methods to provide leads for drug development based on identification and optimization of low affinity ligands from NMR screens have been described. However, leads from these processes still require verification in a bioassay, which is often problematic if compounds have unfavorable optical and solubility properties. A simple method, based on using NMR to observe the activity of the target, is described. It has the advantages of being able to characterize both low affinity leads and a wider selection of compounds in a structure activity relationships series, without the problems affecting a fluorescence assay. In this example we use (31)P to monitor the turnover of a bacterial response regulator, but the generic approach could be applied to other nuclei and thus a range of biological systems.     

Target validation through protein-domain knockout – applications of intracellularly stable single-chain antibodies

The human genome project has delivered a large number of genes and respective proteins that await to be validated as potential drug targets. Such a complexity has made target validation the main bottleneck in today's drug development process. In addition, the majority of these new potential targets are proteins that function intracellularly. Approaches such as gene knockout, antisense RNA or RNA interference (RNAi) are currently used to validate candidate drug targets by analysing the effects of their deletion. Intrabodies (single-chain antibodies expressed within the cell) present an attractive alternative for directly modulating protein function in vivo. In particular, intrabodies can be used to target specific domains of a protein and perform so-called ‘protein-domain knockouts’, thus allowing the dissection of the varied functions of multi-domain proteins.     

Cloning and targeted disruption of two lipopolysaccharide biosynthesis genes, kdsA and waaG, of Pseudomonas aeruginosa PAO1 by site-directed mutagenesis

The emergence of antibiotic resistance in bacterial pathogens poses a great challenge to public health and emphasizes the need for new antimicrobial targets. The recent development of microbial genomics and the availability of genome sequences allows for the identification of essential genes which could be novel and potential targets for antibacterial drugs. However, these predicted targets need experimental validation to confirm essentiality. Here, we report on experimental validation of a two potential targets in the lipopolysaccharide (LPS) biosynthesis pathway of the pathogen Pseudomonas aeruginosa PAO1 using insertion duplication. Two genes, kdsA and waaG, from LPS encoding proteins 2-dehydro-3-deoxyphosphooctonate aldolase and UDP-glucose (heptosyl) LPS α-1,3-glucosyltransferase were selected as putative target candidates for the gene disruption experiments using plasmid insertion mutagenesis to determine essentiality. The introduction of a selectable ampicillin and kanamycin resistance marker into the chromosome resulted in lack of recovery of antibiotic-resistant colonies suggesting the essentiality of these genes for the survival of P. aeruginosa. Several molecular analyses were carried out in order to confirm the essentiality of these genes. We propose that the above two validated drug targets are essential and can be screened for functional inhibitors for the discovery of novel therapeutic compounds against antibiotic-resistant opportunistic pathogen P. aeruginosa.     

High-throughput screening assays for cyclooxygenase-2 and 5-lipoxygenase, the targets for inflammatory disorders

High-throughput screening (HTS) involves testing of compound libraries against validated drug targets using quantitative bioassays to identify 'hit' molecules that modulate the activity of target, which forms the starting point of a drug discovery effort. Eicosanoids formed via cyclooxygenase (COX) and lipoxygenase (LOX) pathways are major players in various inflammatory disorders. As the conventional non-steroidal anti-inflammatory drugs (NSAIDs) that inhibit both the constitutive (COX-1) and the inducible (COX-2) isoforms have gastric and renal side effects and the recently developed COX-2 selective anti-inflammatory drugs (COXIBs) have cardiac side effects, efforts are being made to develop more potent and safer antiinflammatory drugs. Current assay methods for these enzymes, such as oxygraphic, radioisotopic, spectrophotometric etc. are not compatible for screening of large number of compounds as in drug discovery programs. In the present study, HTS-compatible assays for COX-1, COX-2 and 5-LOX were developed for screening of compound libraries with the view to identify potential anti-inflammatory drug candidates. A spectrophotometric assay involving co-oxidation of tetramethyl-p-phenylene diamine (TMPD) during the reduction of prostaglandin G2 (PGG2) to PGH2 was adopted and standardized for screening of compounds against COX-1 and COX-2. Similarly, the HTS-compatible FOX (ferrous oxidation-xylenol orange) based spectrophotometric assay involving the formation of Fe3+/xylenol orange complex showing absorption in the visible range was developed for screening of compounds against 5-LOX.