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Since the advent of high-throughput screening (HTS), there has been an urgent need for methods that facilitate the interrogation of large-scale chemical biology data to build a mode of action (MoA) hypothesis. This can be done either prior to the HTS by subset design of compounds with known MoA or post HTS by data annotation and mining. To enable this process, we developed a tool that compares compounds solely on the basis of their bioactivity: the chemical biological descriptor “high-throughput screening fingerprint” (HTS-FP). In the current embodiment, data are aggregated from 195 biochemical and cell-based assays developed at Novartis and can be used to identify bioactivity relationships among the in-house collection comprising ∼1.5 million compounds. We demonstrate the value of the HTS-FP for virtual screening and in particular scaffold hopping. HTS-FP outperforms state of the art methods in several aspects, retrieving bioactive compounds with remarkable chemical dissimilarity to a probe structure. We also apply HTS-FP for the design of screening subsets in HTS. Using retrospective data, we show that a biodiverse selection of plates performs significantly better than a chemically diverse selection of plates, both in terms of number of hits and diversity of chemotypes retrieved. This is also true in the case of hit expansion predictions using HTS-FP similarity. Sets of compounds clustered with HTS-FP are biologically meaningful, in the sense that these clusters enrich for genes and gene ontology (GO) terms, showing that compounds that are bioactively similar also tend to target proteins that operate together in the cell. HTS-FP are valuable not only because of their predictive power but mainly because they relate compounds solely on the basis of bioactivity, harnessing the accumulated knowledge of a high-throughput screening facility toward the understanding of how compounds interact with the proteome.

We present a novel generic method to better understand the divergent activities of molecules that often occur in orthogonal assays. The newly developed simple prediction of activity differences (SPREAD) method directly aims to model and understand the differences compounds exhibit when tested in two or more assays. By transforming the activity values from the assays into meta‐categories (specifically defined for datasets under scrutiny), statistical models can be trained directly on the qualitative differences between assays. This contributes heavily toward a tangible understanding of molecular assay selectivity. Although ensembles of models could be used alternatively to predict compounds that score highly in one assay and low in another, the advantage of the SPREAD approach is that the chemical features influencing assay differences are parsed out immediately as a consequence of training the model on the coincident assay differences. By training the model that describes the difference between two or more assays, molecular substructures that are responsible for assay selectivity can be parsed out. The method was validated by using four challenging datasets. Copyright © 2009 Wiley Periodicals, Inc., Statistical Analysis and Data Mining 2: 115‐122, 2009

Typically, screening collections of pharmaceutical companies contain more than a million compounds today. However, for certain high-throughput screening (HTS) campaigns, constraints posed by the assay throughput and/or the reagent costs make it impractical to screen the entire deck. Therefore, it is desirable to effectively screen subsets of the collection based on a hypothesis or a diversity selection. How to select compound subsets is a subject of ongoing debate. The authors present an approach based on extended connectivity fingerprints to carry out diversity selection on a per plate basis (instead of a per compound basis). HTS data from 35 Novartis screens spanning 5 target classes were investigated to assess the performance of this approach. The analysis shows that selecting a fingerprint-diverse subset of 250K compounds, representing 20% of the screening deck, would have achieved significantly higher hit rates for 86% of the screens. This measure also outperforms the Murcko scaffold-based plate selection described previously, where only 49% of the screens showed similar improvements. Strikingly, the 2-fold improvement in average hit rates observed for 3 of 5 target classes in the data set indicates a target bias of the plate (and thus compound) selection method. Even though the diverse subset selection lacks any target hypothesis, its application shows significantly better results for some targets—namely, G-protein-coupled receptors, proteases, and protein-protein interactions—but not for kinase and pathway screens. The synthetic origin of the compounds in the diverse subset appears to influence the screening hit rates. Natural products were the most diverse compound class, with significantly higher hit rates compared to the compounds from the traditional synthetic and combinatorial libraries. These results offer empirical guidelines for plate-based diversity selection to enhance hit rates, based on target class and the library type being screened.

Chemogenomics comprises a systematic relationship between targets and ligands that are used as target modulators in living systems such as cells or organisms. In recent years, data on small molecule-bioactivity relationships have become increasingly available, and consequently so have the number of approaches used to translate bioactivity data into knowledge. This review will focus on two aspects of chemogenomics. Firstly, in cases such as cell-based screens, the question of which target(s) a compound is modulating in order to cause the observed phenotype is crucial. In silico target prediction tools can suggest likely biological targets of small molecules via data mining in target-annotated chemical databases. This review presents some of the current tools available for this task and shows some sample applications relevant to a pharmaceutical industry setting. These applications are the prediction of false-positives in cell-based reporter gene assays, the prediction of targets by linking bioassay data with protein domain annotations, and the direct prediction of adverse reactions. Secondly, in recent years a shift from structure-derived chemical descriptors to biological descriptors has occurred. Here, the effect of a compound on a number of biological endpoints is used to make predictions about other properties, such as putative targets, associated adverse reactions, and pathways modulated by the compound. This review further summarizes these “performance” descriptors and their applications, focusing on gene expression profiles and highcontent screening data. The advent of such biological fingerprints suggests that the field of drug discovery is currently at a crossroads, where single target bioassay results are supplanted by multidimensional biological fingerprints that reflect a new awareness of biological networks and polypharmacology.

This work describes a novel semi-sequential technique for in silico enhancement of high-throughput screening (HTS) experiments now employed at Novartis. It is used in situations in which the size of the screen is limited by the readout (e.g., high-content screens) or the amount of reagents or tools (proteins or cells) available. By performing computational chemical diversity selection on a per plate basis (instead of a per compound basis), 25% of the 1,000,000-compound screening was optimized for general initial HTS. Statistical models are then generated from target-specific primary results (percentage inhibition data) to drive the cherry picking and testing from the entire collection. Using retrospective analysis of 11 HTS campaigns, the authors show that this method would have captured on average two thirds of the active compounds (IC50< 10 µM) and three fourths of the active Murcko scaffolds while decreasing screening expenditure by nearly 75%. This result is true for a wide variety of targets, including G-protein-coupled receptors, chemokine receptors, kinases, metalloproteinases, pathway screens, and protein-protein interactions. Unlike time-consuming “classic” sequential approaches that require multiple iterations of cherry picking, testing, and building statistical models, here individual compounds are cherry picked just once, based directly on primary screening data. Strikingly, the authors demonstrate that models built from primary data are as robust as models built from IC50data. This is true for all HTS campaigns analyzed, which represent a wide variety of target classes and assay types.

The noise level of a high-throughput screening (HTS) experiment depends on various factors such as the quality and robustness of the assay itself and the quality of the robotic platform. Screening of compound mixtures is noisier than screening single compounds per well. A classification model based on naïve Bayes (NB) may be used to enrich such data. The authors studied the ability of the NB classifier to prioritize noisy primary HTS data of compound mixtures (5 compounds/well) in 4 campaigns in which the percentage of noise presumed to be inactive compounds ranged between 81% and 91%. The top 10% of the compounds suggested by the classifier captured between 26% and 45% of the active compounds. These results are reasonable and useful, considering the poor quality of the training set and the short computing time that is needed to build and deploy the classifier. (Journal of Biomolecular Screening2004:32-36)

Objectives: The objectives of this study were: (i) to improve general practitioners' knowledge of the mental disorders they commonly treat, and to increase their confidence in managing people with these disorders; and (ii) to increase general practitioners' familiarity with the Logan-Beaudesert Mental Health Service.Method: Eleven general practitioners met with psychiatrists from the Logan-Beaudesert Mental Health Service in six 2-hour sessions held at monthly intervals. Each session comprised a teaching component, a consumer interview and a case-conference. Outcomes were measured using an objective test of general practitioners' knowledge, a subjective test of their confidence in dealing with mental health problems, and satisfaction surveys for participating consumers, general practitioners and psychiatrists.Results: On the objective test, the scores of 10 out of the 11 general practitioners improved (p < 0.05). On the subjective test, the ranked scores improved in nine out of the 11 cases (p < 0.05). Consumers, general practitioners and psychiatrists expressed their satisfaction with the format and content of the course.Conclusions: Having improved the knowledge of a group of general practitioners who are familiar with the functioning of the Logan-Beaudeserl Mental Health Service, the stage is now set to proceed to the next step: the shared-care project.

ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.

ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.

Rubella vaccine was introduced in Canada in 1969. Immunization practices and vaccine coverage varied from province to province. In the 1970s the Canadian National Advisory Committee on Immunization endorsed both the policy of mass vaccination - in combination with measles and mumps vaccines - for infants, which seven provinces followed, and that of selective immunization of prepubertal girls, which three provinces followed. In 1982, the Committee advocated a comprehensive policy that incorporated the best features of the two policies and also increased the emphasis on immunization of susceptible adolescent and adult women. As of 1983, in all provinces the vaccine has been routinely administered to infants 12–15 months old; in seven, also to prepubertal girls. After the introduction of rubella vaccine, rubella incidence declined markedly, but the endemic level of rubella incidence remained unchanged. Congenital rubella syndrome (CRS) was added to the federal list of notifiable diseases only in 1979. Sixty-seven CRS cases were reported by five provinces from 1979 to 1983, during which a trend of declining CRS incidence rates (per 100,000 live births) was indicated. However, the numbers of cases are too small to draw definite conclusions regarding the impact of immunization programs.