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Grasslands globally deliver many ecosystem services, including water management to alleviate flood risk reduction. Two replicated field experiments were conducted to study how agricultural forage species with diverse rooting systems, sown as single species, affected rooting, soil structure and earthworm populations, and consequently water infiltration to understand how they each might influence flood risk from grasslands. Experiment One showed soils under red clover (Trifolium pratense), white clover (Trifolium repens) and chicory (Cichorium intybus) had higher infiltration rates three years after establishment, compared to perennial ryegrass (Lolium perenne). Higher red clover and chicory root biomass or increased earthworm abundance under white clover may have caused these effects. Experiment Two monitored infiltration at intervals over several years post establishment to understand the timeframe for changes in rates; plantain (Plantago lanceolata) was sown as an additional forage. Infiltration declined post establishment, the timing and extent of decline varying with forages; forage effects were significant after 27 months (P < 0.05). Infiltration rates were higher under red and white clover compared to ryegrass, with chicory and plantain intermediate (P < 0.05). Forages again differed in likely mechanisms delivering higher water infiltration, notably between the two clover species. White clover had higher earthworm biomass (P < 0.05), whereas red clover had a higher average root diameter compared to the other forages (P < 0.05). Drivers of intermediate benefits of chicory and plantain also differed: chicory had higher earthworm abundance (month 38) compared to plantain, which had higher average root diameter compared to ryegrass (month 41); 30 months post-establishment soil bulk density was lower under both forages compared to ryegrass and red clover, with white clover intermediate (P < 0.05); bulk density and penetration resistance did not relate to infiltration. Findings demonstrate that a shift from perennial ryegrass-dominated pastures to swards with more contrasting forages provides an ecohydrological approach to mitigating flood risk and climate adaptation., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Christina Marley reports The PROSOILplus project received funding through the Welsh Government Rural Communities - Rural Development Programme 2014–2020, which is funded by the European Agricultural Fund for Rural Development and the Welsh Government. Christina Marley reports IBERS receives strategic funding from BBSRC, UKRI. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The efficient preservation of protein in silage for livestock feed is dependent on the rate and extent of proteolysis. Previous research on fresh forage indicated enhanced protein stability in certain Festulolium (ryegrass × fescue hybrids) cultivars compared to ryegrass. This is the first report of an experiment to test the hypothesis that a Lolium perenne × Festuca arundinacea var glaucescens cultivar had reduced proteolysis compared to perennial ryegrass ( L. perenne ) during the ensiling process. Forages were harvested in May (Cut 2) and August (Cut 4), wilted for 24 h and ensiled in laboratory-scale silos. Silage was destructively sampled at 0 h, 9 h, 24 h, 48 h, 72 h, 14 days and 90 days post-ensiling, and dry matter (DM), pH and chemical composition were determined. At Cut 2, there was no difference in crude protein between treatments but ryegrass had higher soluble nitrogen (SN) ( P  < 0.001) and grass × time interactions ( p  = 0.03) indicated higher rates of proteolysis. By Cut 4, Festulolium had (5.5% units) higher CP than ryegrass ( p  < 0.001) but SN did not differ. Ammonia-N did not differ between silages in either cut. DM differences (11.8% units) between treatments in Cut 4 (v.2.2% in Cut 2) may have masked effects on proteolysis, highlighting the importance of management on silage quality. This was despite higher WSC in ryegrass in both cuts ( p <  0.001), with grass × time interactions (Cut 2; p  = 0.03) showing slower WSC decline in ryegrass in Cut 4 ( p  < 0.001). Silage pH values did not differ between grasses in either cut, but grass × time interactions ( p  < 0.001) showed a slower decline in both ryegrass cuts, resulting in higher ( p  < 0.05) pH at 24 h and 72 h for Cuts 2 and 4, respectively. Overall, the hypothesis for an enhanced protein stability in Festulolium when ensiled as ruminant feed was evidenced by lower SN but not ammonia-N in an early-cut silage with a comparable DM to ryegrass., Competing Interests: The authors have stated explicitly that there are no conflicts of interest in connection with this article., (© 2023 The Authors. Food and Energy Security published by John Wiley & Sons Ltd.)

The increasing frequency of droughts and floods on grasslands, due to climate change, increases the risk of soil compaction. Soil compaction affects both soil and forage productivity. Differing grasses may counteract some effects of compaction due to differences in their root architecture and ontogeny. To compare their resilience to soil compaction, three Festulolium (ryegrass and fescue species' hybrids) forage grass cultivars comprising differing root architecture and ontogeny were compared in replicated field plots, together with a ryegrass and tall fescue variety as controls. Pre-compaction soil and forage properties were determined in spring using > four-year-old plots to generate baseline data. Half of each field plot was then artificially compacted using farm machinery. Forage dry matter yield (DMY) was determined over four cuts. After the final harvest, post compaction soil characteristics and root biomass (RB) were compared between grasses in the non-compacted and compacted soils. Pre-compaction data showed that soil under Festulolium and ryegrass had similar water infiltration rates, higher than soil under tall fescue plots. Tiller density of the Festulolium at this time was significantly higher than fescue but not the ryegrass control. Forage DMY was significantly lower ( p  < .001) with compacted soil at the first cut but, by the completion of the growing season, there was no effect of soil compaction on total DMY. Tall fescue had a higher total DMY than other grasses, which all produced similar annual yields. Soil bulk density and penetration resistance were higher, and grass tiller density was lower in compacted soils. Root biomass in compacted soils showed a tendency for Festulolium cv Lp × Fg to have higher RB than the ryegrass at 0-15 cm depth. Overall, findings showed alternative grass root structures provide differing resilience to machinery compaction, and root biomass production can be encouraged without negative impacts on forage productivity., Competing Interests: None declared., (© 2020 The Authors. Food and Energy Security published by John Wiley & Sons Ltd. and the Association of Applied Biologists.)

Research has shown both production and health benefits for the use of chicory (Cichorium intybus) within ruminant diets. Despite this, little was known about the effects of this forage, containing differing fatty acid profiles and secondary plant compounds compared with ryegrass, on beef stability, fatty acid composition or sensory properties. An experiment was conducted to investigate whether the inclusion of chicory in the diet of grazing beef steers would alter these three properties in the M. Longissimus muscle when compared with beef steers grazing perennial ryegrass (Lolium perenne). Triplicate 2 ha plots were established with a chicory (cv. Puna II)/perennial ryegrass mix or a perennial ryegrass control. A core group of 36 Belgian Blue - cross steers were used within a 2-year beef finishing experiment (n=6/replicate plot). In the 2nd grazing year, steers were slaughtered as they reached a target fat class of 3. Muscle pH was checked 2 and 48 h post-slaughter. A section of the hindloin joint containing the M. Longissimus lumborum muscle was removed and a 20 mm-thick steak was cut and muscle samples were taken for analysis of vitamin E and fatty acid analysis. The remaining section of the loin was vacuum packed in modified atmosphere packs and subjected to simulated retail display. A section of the conditioned loin was used for sensory analysis. Data on pH, vitamin E concentration and colour stability in a simulated retail display showed there were no effects of including chicory in the diet of grazing beef steers on meat stability. There were also no differences found in the fatty acid composition or the overall eating quality of the steaks from the two treatments. In conclusion, there were no substantive effects of including chicory in the swards of grazing beef cattle on meat stability, fatty acid composition or sensory properties of the M. Longissimus muscle when compared with beef steers grazing ryegrass-only swards.

Motivation: Identifying phenotypes based on high-content cellular images is challenging. Conventional image analysis pipelines for phenotype identification comprise multiple independent steps, with each step requiring method customization and adjustment of multiple parameters., Results: Here, we present an approach based on a multi-scale convolutional neural network (M-CNN) that classifies, in a single cohesive step, cellular images into phenotypes by using directly and solely the images' pixel intensity values. The only parameters in the approach are the weights of the neural network, which are automatically optimized based on training images. The approach requires no a priori knowledge or manual customization, and is applicable to single- or multi-channel images displaying single or multiple cells. We evaluated the classification performance of the approach on eight diverse benchmark datasets. The approach yielded overall a higher classification accuracy compared with state-of-the-art results, including those of other deep CNN architectures. In addition to using the network to simply obtain a yes-or-no prediction for a given phenotype, we use the probability outputs calculated by the network to quantitatively describe the phenotypes. This study shows that these probability values correlate with chemical treatment concentrations. This finding validates further our approach and enables chemical treatment potency estimation via CNNs., Availability and Implementation: The network specifications and solver definitions are provided in Supplementary Software 1., Contact: william&#95;jose.godinez&#95;navarro@novartis.com or xian-1.zhang@novartis.com., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

High-throughput screening (HTS) is an integral part of early drug discovery. Herein, we focused on those small molecules in a screening collection that have never shown biological activity despite having been exhaustively tested in HTS assays. These compounds are referred to as 'dark chemical matter' (DCM). We quantified DCM, validated it in quality control experiments, described its physicochemical properties and mapped it into chemical space. Through analysis of prospective reporter-gene assay, gene expression and yeast chemogenomics experiments, we evaluated the potential of DCM to show biological activity in future screens. We demonstrated that, despite the apparent lack of activity, occasionally these compounds can result in potent hits with unique activity and clean safety profiles, which makes them valuable starting points for lead optimization efforts. Among the identified DCM hits was a new antifungal chemotype with strong activity against the pathogen Cryptococcus neoformans but little activity at targets relevant to human safety.

Vast amounts of bioactivity data have been generated for small molecules across public and corporate domains. Biological signatures, either derived from systematic profiling efforts or from existing historical assay data, have been successfully employed for small molecule mechanism-of-action elucidation, drug repositioning, hit expansion and screening subset design. This article reviews different types of biological descriptors and applications, and we demonstrate how biological data can outlive the original purpose or project for which it was generated. By comparing 150 HTS campaigns run at Novartis over the past decade on the basis of their active and inactive chemical matter, we highlight the opportunities and challenges associated with cross-project learning in drug discovery., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

An experiment investigated whether the inclusion of chicory (Cichorium intybus) in swards grazed by beef steers altered their performance, carcass characteristics or parasitism when compared to steers grazing perennial ryegrass (Lolium perenne). Triplicate 2-ha plots were established with a chicory/ryegrass mix or ryegrass control. Forty-eight Belgian Blue-cross steers were used in the first grazing season and a core group (n = 36) were retained for finishing in the second grazing season. The experiment comprised of a standardisation and measurement period. During standardisation, steers grazed a ryegrass/white clover pasture as one group. Animals were allocated to treatment on the basis of liveweight, body condition and faecal egg counts (FEC) determined 7 days prior to the measurement period. The measurement period ran from 25 May until 28 September 2010 and 12 April until 11 October 2011 in the first and second grazing year. Steers were weighed every 14 days at pasture or 28 days during housing. In the first grazing year, faecal samples were collected for FEC and parasite cultures. At the end of the first grazing year, individual blood samples were taken to determine O. ostertagi antibody and plasma pepsinogen levels. During winter, animals were housed as one group and fed silage. In the second grazing year, steers were slaughtered when deemed to reach fat class 3. Data on steer performance showed no differences in daily live-weight gain which averaged 1.04 kg/day. The conformation, fat grade and killing out proportion of beef steers grazing chicory/ryegrass or ryegrass were not found to differ. No differences in FEC, O. ostertagi antibody or plasma pepsinogen levels of beef steers grazing either chicory/ryegrass or ryegrass were observed. Overall, there were no detrimental effects of including chicory in swards grazed by beef cattle on their performance, carcass characteristics or helminth parasitism, when compared with steers grazing ryegrass.

The increasing amount of chemogenomics data, that is, activity measurements of many compounds across a variety of biological targets, allows for better understanding of pharmacology in a broad biological context. Rather than assessing activity at individual biological targets, today understanding of compound interaction with complex biological systems and molecular pathways is often sought in phenotypic screens. This perspective poses novel challenges to structure-activity relationship (SAR) assessment. Today, the bottleneck of drug discovery lies in the understanding of SAR of rich datasets that go beyond single targets in the context of biological pathways, potential off-targets, and complex selectivity profiles. To aid in the understanding and interpretation of such complex SAR, we introduce Chemotography (chemotype chromatography), which encodes chemical space using a color spectrum by combining clustering and multidimensional scaling. Rich biological data in our approach were visualized using spatial dimensions traditionally reserved for chemical space. This allowed us to analyze SAR in the context of target hierarchies and phylogenetic trees, two-target activity scatter plots, and biological pathways. Chemotography, in combination with the Kyoto Encyclopedia of Genes and Genomes (KEGG), also allowed us to extract pathway-relevant SAR from the ChEMBL database. We identified chemotypes showing polypharmacology and selectivity-conferring scaffolds, even in cases where individual compounds have not been tested against all relevant targets. In addition, we analyzed SAR in ChEMBL across the entire Kinome, going beyond individual compounds. Our method combines the strengths of chemical space visualization for SAR analysis and graphical representation of complex biological data. Chemotography is a new paradigm for chemogenomic data visualization and its versatile applications presented here may allow for improved assessment of SAR in biological context, such as phenotypic assay hit lists., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

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 here a comprehensive analysis of proteases in the peptide substrate space and demonstrate its applicability for lead discovery. Aligned octapeptide substrates of 498 proteases taken from the MEROPS peptidase database were used for the in silico analysis. A multiple-category naïve Bayes model, trained on the two-dimensional chemical features of the substrates, was able to classify the substrates of 365 (73%) proteases and elucidate statistically significant chemical features for each of their specific substrate positions. The positional awareness of the method allows us to identify the most similar substrate positions between proteases. Our analysis reveals that proteases from different families, based on the traditional classification (aspartic, cysteine, serine, and metallo), could have substrates that differ at the cleavage site (P1-P1') but are similar away from it. Caspase-3 (cysteine protease) and granzyme B (serine protease) are previously known examples of cross-family neighbors identified by this method. To assess whether peptide substrate similarity between unrelated proteases could reliably translate into the discovery of low molecular weight synthetic inhibitors, a lead discovery strategy was tested on two other cross-family neighbors--namely cathepsin L2 and matrix metallo proteinase 9, and calpain 1 and pepsin A. For both these pairs, a naïve Bayes classifier model trained on inhibitors of one protease could successfully enrich those of its neighbor from a different family and vice versa, indicating that this approach could be prospectively applied to lead discovery for a novel protease target with no known synthetic inhibitors.

Background: Transient receptor potential vanilloid subtype 1 (TRPV1) receptor is a primary pain-sensing relay at peripheral sensory nerve endings and is also widespread in the brain, where it is implicated in neurodegeneration. Previous studies of TRPV1 neurotoxicity have utilized heterogeneous receptor populations, non-selective ligands, or non-neuronal cell types. Here, we explored the pharmacology of TRPV1-induced cytotoxicity in a homogeneous, neurone-like cellular environment., Methods: Cell death was examined in a human neurone-like cell line, stably expressing recombinant human TRPV1. Cytotoxicity was quantified in terms of nuclear morphology and mitochondrial complex II activity. Immunocytochemical markers of apoptotic cell death were also examined., Results: The TRPV1-selective agonist capsaicin, and the endovanilloids anandamide and N-arachidonoyl-dopamine (NADA), induced TRPV1-dependent delayed cell death in a concentration- and time-dependent manner. Capsaicin exposure time was significantly correlated with potency (r(2)=0.91, P=0.01). Release of cytochrome c from mitochondria, activation of caspase-3, and condensed nuclear chromatin were evident 6 h after capsaicin exposure, but cytotoxicity was unaffected by a pan-caspase inhibitor (zVAD-fmk, 50 microM)., Conclusions: We conclude that capsaicin, anandamide, and NADA can initiate TRPV1-dependent delayed cell death in neurone-like cells. This is an apoptosis-like process, but independent of caspase activity.

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.

We present a novel method to better investigate adverse drug reactions in chemical space. By integrating data sources about adverse drug reactions of drugs with an established cheminformatics modeling method, we generate a data set that is then visualized with a systems biology tool. Thereby new insights into undesired drug effects are gained. In this work, we present a global analysis linking chemical features to adverse drug reactions.

The elucidation of drug targets is important both to optimize desired compound action and to understand drug side-effects. In this study, we created statistical models which link chemical substructures of ligands to protein domains in a probabilistic manner and employ the model to triage the results of affinity chromatography experiments. By annotating targets with their InterPro domains, general rules of ligand-protein domain associations were derived and successfully employed to predict protein targets outside the scope of the training set. This methodology was then tested on a proteomics affinity chromatography data set containing 699 compounds. The domain prediction model correctly detected 31.6% of the experimental targets at a specificity of 46.8%. This is striking since 86% of the predicted targets are not part of them (but share InterPro domains with them), and thus could not have been predicted by conventional target prediction approaches. Target predictions improve drastically when significance (FDR) scores for target pulldowns are employed, emphasizing their importance for eliminating artifacts. Filament proteins (such as actin and tubulin) are detected to be 'frequent hitters' in proteomics experiments and their presence in pulldowns is not supported by the target predictions. On the other hand, membrane-bound receptors such as serotonin and dopamine receptors are noticeably absent in the affinity chromatography sets, although their presence would be expected from the predicted targets of compounds. While this can partly be explained by the experimental setup, we suggest the computational methods employed here as a complementary step of identifying protein targets of small molecules. Affinity chromatography results for gefitinib are discussed in detail and while two out of the three kinases with the highest affinity to gefitinib in biochemical assays are detected by affinity chromatography, also the possible involvement of NSF as a target for modulating cancer progressions via beta-arrestin can be proposed by this method.

We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.

Different molecular descriptors capture different aspects of molecular structures, but this effect has not yet been quantified systematically on a large scale. In this work, we calculate the similarity of 37 descriptors by repeatedly selecting query compounds and ranking the rest of the database. Euclidean distances between the rank-ordering of different descriptors are calculated to determine descriptor (as opposed to compound) similarity, followed by PCA for visualization. Four broad descriptor classes are identified, which are circular fingerprints; circular fingerprints considering counts; path-based and keyed fingerprints; and pharmacophoric descriptors. Descriptor behavior is much more defined by those four classes than the particular parametrization. Using counts instead of the presence/absence of fingerprints significantly changes descriptor behavior, which is crucial for performance of topological autocorrelation vectors, but not circular fingerprints. Four-point pharmacophores (piDAPH4) surprisingly lead to much higher retrieval rates than three-point pharmacophores (28.21% vs 19.15%) but still similar rank-ordering of compounds (retrieval of similar actives). Looking into individual rankings, circular fingerprints seem more appropriate than path-based fingerprints if complex ring systems or branching patterns are present; count-based fingerprints could be more suitable in databases with a large number of repeated subunits (amide bonds, sugar rings, terpenes). Information-based selection of diverse fingerprints for consensus scoring (ECFP4/TGD fingerprints) led only to marginal improvement over single fingerprint results. While it seems to be nontrivial to exploit orthogonal descriptor behavior to improve retrieval rates in consensus virtual screening, those descriptors still each retrieve different actives which corroborates the strategy of employing diverse descriptors individually in prospective virtual screening settings.

High-throughput screening (HTS) is a well-established hit-finding approach used in the pharmaceutical industry. In this article, recent experience at Novartis with respect to factors influencing the success of HTS campaigns is discussed. An inherent measure of HTS quality could be defined by the assay Z and Z' factors, the number of hits and their biological potencies; however, such measures of quality do not always correlate with the advancement of hits to the later stages of drug discovery. Also, for many target classes, such as kinases, it is easy to identify hits, but, as a result of selectivity, intellectual property and other issues, the projects do not result in lead declarations. In this article, HTS success is defined as the fraction of HTS campaigns that advance into the later stages of drug discovery, and the major influencing factors are examined. Interestingly, screening compounds in individual wells or in mixtures did not have a major impact on the HTS success and, equally interesting, there was no difference in the progression rates of biochemical and cell-based assays. Particular target types, assay technologies, structure-activity relationships and powder availability had a much greater impact on success as defined above. In addition, significant mutual dependencies can be observed - while one assay format works well with one target type, this situation might be completely reversed for a combination of the same readout technology with a different target type. The results and opinions presented here should be regarded as groundwork, and a plethora of factors that influence the fate of a project, such as biophysical measurements, chemical attractiveness of the hits, strategic reasons and safety pharmacology, are not covered here. Nonetheless, it is hoped that this information will be used industry-wide to improve success rates in terms of hits progressing into exploratory chemistry and beyond. The support that can be obtained from new in silico approaches to phase transitions are also described, along with the gaps they are designed to fill.

In this work we explore the possibilities of using fragment-based screening data to prioritize compounds from a full HTS library, a method we call virtual fragment linking (VFL). The ability of VFL to identify compounds of nanomolar potency based on micromolar fragment binding data was tested on 75 target classes from the WOMBAT database and succeeded in 57 cases. Further, the method was demonstrated for seven drug targets from in-house screening programs that performed both FBS of 8800 fragments and screens of the full library. VFL captured between 28% and 67% of the hits (IC 50 < 10microM) in the top 5% of the ranked library for four of the targets (enrichment between 5-fold and 13-fold). Our findings lead us to conclude that proper coverage of chemical space by the fragment library is crucial for the VFL methodology to be successful in prioritizing HTS libraries from fragment-based screening data.

Palladium nanoparticles were entrapped within resin plugs and used in a range of ligand-free cross-coupling reactions; the convenient modular format of the resin plug enhanced resin handling and allowed the catalysts to be easily recovered and multiply reused.

Development of a pharmacophore hypothesis related to small-molecule activity is pivotal to chemical optimization of a series, since it defines features beneficial or detrimental to activity. Although crystal structures may provide detailed 3D interaction information for one molecule with its receptor, docking a different ligand to that model often leads to unreliable results due to protein flexibility. Graham Richards' lab was one of the first groups to utilize "fuzzy" pattern recognition algorithms taken from the field of image processing to solve problems in protein modeling. Thus, descriptor "fuzziness" was partly able to emulate conformational flexibility of the target while simultaneously enhancing the speed of the search. In this work, we extend these developments to a ligand-based method for describing and aligning molecules in flexible chemical space termed FEature POint PharmacophoreS (FEPOPS), which allows exploration of dynamic biological space. We develop a novel, combinatorial algorithm for molecular comparisons and evaluate it using the WOMBAT dataset. The new approach shows superior retrospective virtual screening performance than earlier shape-based or charge-based algorithms. Additionally, we use target prediction to evaluate how FEPOPS alignments match the molecules biological activity by identifying the atoms and features that make the key contributions to overall chemical similarity. Overall, we find that FEPOPS are sufficiently fuzzy and flexible to find not only new ligand scaffolds, but also challenging molecules that occupy different conformational states of dynamic biological space as from induced fits.

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 high-content 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.

High throughput screening (HTS) data is often noisy, containing both false positives and negatives. Thus, careful triaging and prioritization of the primary hit list can save time and money by identifying potential false positives before incurring the expense of followup. Of particular concern are cell-based reporter gene assays (RGAs) where the number of hits may be prohibitively high to be scrutinized manually for weeding out erroneous data. Based on statistical models built from chemical structures of 650 000 compounds tested in RGAs, we created "frequent hitter" models that make it possible to prioritize potential false positives. Furthermore, we followed up the frequent hitter evaluation with chemical structure based in silico target predictions to hypothesize a mechanism for the observed "off target" response. It was observed that the predicted cellular targets for the frequent hitters were known to be associated with undesirable effects such as cytotoxicity. More specifically, the most frequently predicted targets relate to apoptosis and cell differentiation, including kinases, topoisomerases, and protein phosphatases. The mechanism-based frequent hitter hypothesis was tested using 160 additional druglike compounds predicted by the model to be nonspecific actives in RGAs. This validation was successful (showing a 50% hit rate compared to a normal hit rate as low as 2%), and it demonstrates the power of computational models toward understanding complex relations between chemical structure and biological function.

Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.

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 (IC(50) < 10 microM) 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 IC(50) data. This is true for all HTS campaigns analyzed, which represent a wide variety of target classes and assay types.

Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities. In practice, effectively choosing a similarity metric is case dependent. This work compares both 2D and 3D chemical descriptors as tools for predicting the biological targets of ligand probes, on the basis of their similarity to reference molecules in a 46,000 compound, biologically annotated chemical database. Overall, we found that the 2D methods employed here outperform the 3D (88% vs 67% success) in correct target prediction. However, the 3D descriptors proved superior in cases of probes with low structural similarity to other compounds in the database (singletons). Additionally, the 3D method (FEPOPS) shows promise for providing pharmacophoric alignment of the small molecules' chemical features consistent with those seen in experimental ligand/ receptor complexes. These results suggest that querying annotated chemical databases with a systematic combination of both 2D and 3D descriptors will prove more effective than employing single methods.

Conventional similarity searching of molecules compares single (or multiple) active query structures to each other in a relative framework, by means of a structural descriptor and a similarity measure. While this often works well, depending on the target, we show here that retrieval rates can be improved considerably by incorporating an external framework describing ligand bioactivity space for comparisons ("Bayes affinity fingerprints"). Structures are described by Bayes scores for a ligand panel comprising about 1000 activity classes extracted from the WOMBAT database. The comparison of structures is performed via the Pearson correlation coefficient of activity classes, that is, the order in which two structures are similar to the panel activity classes. Compound retrieval on a recently published data set could be improved by as much as 24% relative (9% absolute). Knowledge about the shape of the "bioactive chemical universe" is thus beneficial to identifying similar bioactivities. Principal component analysis was employed to further analyze activity space with the objective to define orthogonal ligand bioactive chemical space, leading to nine major (roughly orthogonal) activity axes. Employing only those nine activity classes, retrieval rates are still comparable to original Bayes affinity fingerprints; thus, the concept of orthogonal bioactive ligand chemical space was validated as being an information-rich but low-dimensional representation of bioactivity space. Correlations between activity classes are a major determinant to gauge whether the desired multitarget activity of drugs is (on the basis of current knowledge) a feasible concept because it measures the extent to which activities can be optimized independently, or only by strongly influencing one another.

Lead discovery in the pharmaceutical environment is largely an industrial-scale process in which it is typical to screen 1-5 million compounds in a matter of weeks using High Throughput Screening (HTS). This process is a very costly endeavor. Typically a HTS campaign of 1 million compounds will cost anywhere from $500000 to $1000000. There is consequently a great deal of pressure to maximize the return on investment by finding fast and more effective ways to screen. A panacea that has emerged over the past few years to help address this issue is in silico screening. In silico screening is now incorporated in all areas of lead discovery; from target identification and library design, to hit analysis and compound profiling. However, as lead discovery has evolved over the past few years, so has the role of in silico screening.

Target identification is a critical step following the discovery of small molecules that elicit a biological phenotype. The present work seeks to provide an in silico correlate of experimental target fishing technologies in order to rapidly fish out potential targets for compounds on the basis of chemical structure alone. A multiple-category Laplacian-modified naïve Bayesian model was trained on extended-connectivity fingerprints of compounds from 964 target classes in the WOMBAT (World Of Molecular BioAcTivity) chemogenomics database. The model was employed to predict the top three most likely protein targets for all MDDR (MDL Drug Database Report) database compounds. On average, the correct target was found 77% of the time for compounds from 10 MDDR activity classes with known targets. For MDDR compounds annotated with only therapeutic or generic activities such as "antineoplastic", "kinase inhibitor", or "anti-inflammatory", the model was able to systematically deconvolute the generic activities to specific targets associated with the therapeutic effect. Examples of successful deconvolution are given, demonstrating the usefulness of the tool for improving knowledge in chemogenomics databases and for predicting new targets for orphan compounds.

High-throughput screening (HTS) plays a pivotal role in lead discovery for the pharmaceutical industry. In tandem, cheminformatics approaches are employed to increase the probability of the identification of novel biologically active compounds by mining the HTS data. HTS data is notoriously noisy, and therefore, the selection of the optimal data mining method is important for the success of such an analysis. Here, we describe a retrospective analysis of four HTS data sets using three mining approaches: Laplacian-modified naive Bayes, recursive partitioning, and support vector machine (SVM) classifiers with increasing stochastic noise in the form of false positives and false negatives. All three of the data mining methods at hand tolerated increasing levels of false positives even when the ratio of misclassified compounds to true active compounds was 5:1 in the training set. False negatives in the ratio of 1:1 were tolerated as well. SVM outperformed the other two methods in capturing active compounds and scaffolds in the top 1%. A Murcko scaffold analysis could explain the differences in enrichments among the four data sets. This study demonstrates that data mining methods can add a true value to the screen even when the data is contaminated with a high level of stochastic noise.

A primary goal of 3D similarity searching is to find compounds with similar bioactivity to a reference ligand but with different chemotypes, i.e., "scaffold hopping". However, an adequate description of chemical structures in 3D conformational space is difficult due to the high-dimensionality of the problem. We present an automated method that simplifies flexible 3D chemical descriptions in which clustering techniques traditionally used in data mining are exploited to create "fuzzy" molecular representations called FEPOPS (feature point pharmacophores). The representations can be used for flexible 3D similarity searching given one or more active compounds without a priori knowledge of bioactive conformations or pharmacophores. We demonstrate that similarity searching with FEPOPS significantly enriches for actives taken from in-house high-throughput screening datasets and from MDDR activity classes COX-2, 5-HT3A, and HIV-RT, while also scaffold or ring-system hopping to new chemical frameworks. Further, inhibitors of target proteins (dopamine 2 and retinoic acid receptor) are recalled by FEPOPS by scaffold hopping from their associated endogenous ligands (dopamine and retinoic acid). Importantly, the method excels in comparison to commonly used 2D similarity methods (DAYLIGHT, MACCS, Pipeline Pilot fingerprints) and a commercial 3D method (Pharmacophore Distance Triplets) at finding novel scaffold classes given a single query molecule.

We have previously reported that the application of a Laplacian-modified naive Bayesian (NB) classifier may be used to improve the ranking of known inhibitors from a random database of compounds after High-Throughput Docking (HTD). The method relies upon the frequency of substructural features among the active and inactive compounds from 2D fingerprint information of the compounds. Here we present an investigation of the role of extended connectivity fingerprints in training the NB classifier against HTD studies on the HIV-1 protease using three docking programs: Glide, FlexX, and GOLD. The results show that the performance of the NB classifier is due to the presence of a large number of features common to the set of known active compounds rather than a single structural or substructural scaffold. We demonstrate that the Laplacian-modified naive Bayesian classifier trained with data from high-throughput docking is superior at identifying active compounds from a target database in comparison to conventional two-dimensional substructure search methods alone.

We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.

The technology underpinning high-throughput docking (HTD) has developed over the past few years to where it has become a vital tool in modern drug discovery. Although the performance of various docking algorithms is adequate, the ability to accurately and consistently rank compounds using a scoring function remains problematic. We show that by employing a simple machine learning method (naïve Bayes) it is possible to significantly overcome this deficiency. Compounds from the Available Chemical Directory (ACD), along with known active compounds, were docked into two protein targets using three software packages. In cases where HTD alone was able to show some enrichment, the application of naïve Bayes was able to improve upon the enrichment. The application of this methodology to enrich HTD results can be carried out without a priori knowledge of the activity of compounds and results in superior enrichment of known actives compared to the use of scoring methods alone.

Angiogenesis is crucial for tumor growth. Angiogenesis inhibitors, such as O-(chloracetyl-carbamoyl) fumagillol (TNP-470), are thus emerging as a new class of anticancer drugs. In clinical trials, TNP-470 slowed tumor growth in patients with metastatic cancer. However, at higher doses necessary for tumor regression, many patients experienced neurotoxicity. We therefore synthesized and characterized a water-soluble conjugate of N-(2-hydroxypropyl)methacrylamide (HPMA) copolymer, Gly-Phe-Leu-Gly linker and TNP-470. This conjugate accumulated selectively in tumor vessels because of the enhanced permeability and retention (EPR) effect. HPMA copolymer-TNP-470 substantially enhanced and prolonged the activity of TNP-470 in vivo in tumor and hepatectomy models. Polymer conjugation prevented TNP-470 from crossing the blood-brain barrier (BBB) and decreased its accumulation in normal organs, thereby avoiding drug-related toxicities. Treatment with TNP-470 caused weight loss and neurotoxic effects in mice, whereas treatment with the conjugate did not. This new approach for targeting angiogenesis inhibitors specifically to the tumor vasculature may provide a new strategy for the rational design of cancer therapies.

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.

SUMMARY Transgenic peas containing a copy of the Pea seed-borne mosaic virus (PSbMV) isolate DPD1 NIb sequence develop a 'recovery' phenotype when challenged with either the homologous (DPD1) or a heterologous (NY) PSbMV isolate. However, the specificity of the subsequent resistance differs with respect to the initiation by and targeting of different virus isolates. Analysis of tissue in which recovery had been induced by either of the isolates, revealed the presence of low molecular RNA molecules (siRNA) derived from degradation of the NIb transgene mRNA. When non-transgenic scions were grafted on to transgenic stocks in which recovery had been induced, all the scions became infected, indicating that the virus can be exported from recovered tissue. Experiments in which recovered scions were grafted on to non-transgenic stocks revealed that the recovery phenotype could be maintained in the apparent absence of a source of virus. However in a number of cases, side-shoots which developed on the non-transgenic stock became infected. These results indicate that recovered tissue contains extremely low levels of infectious virus with the potential, directly or indirectly, to confer the observed resistance specificity. Indirectly, the viral genome could act as a source of specific siRNA molecules, which are present in infected tissues but are below the level of detection in recovered tissues. These could act in conjunction with siRNAs derived from the transgene mRNA to maintain a level of PTGS (post-transcriptional gene silencing) activity which is effective in preventing further accumulation of homologous or related viruses. We suggest a model to explain the differential specificity.

Successful nucleophilic substitution at a methylene attached to the bridgehead (1-) position of the 2-azabicyclo[2.1.1]hexane ring system opens the way to construction of novel derivatives having a wider range of functional groups attached to the 1-position via a methylene "spacer" (including the beta-amino acid homologue of 2,4-methanoproline) and provides access to epibatidine analogues containing heterocyclic substituents and also to further homologation at the 1-position. Displacements with loss of a nucleofuge (e.g., loss of mesylate anion from the 1-mesyloxymethyl derivative) require thermal activation but proceed without the rearrangement initially anticipated in such a strained bicyclic ring system. A novel tricyclic carbamate intermediate 17 has been isolated; nucleophilic attack on 17 leads directly to the isolation of N-deprotected substitution products (with concomitant decarboxylation).

Polymer-directed enzyme prodrug therapy (PDEPT) is a novel two-step antitumor approach that uses a combination of a polymeric prodrug and polymer-enzyme conjugate to generate a cytotoxic drug rapidly and selectively at the tumor site. Previously we have shown that N-(2-hydroxypropyl)methacrylamide (HPMA) copolymer-bound cathepsin B can release doxorubicin intratumorally from an HPMA copolymer conjugate PK1. Here we describe for the first time the synthesis and biological characterization of a PDEPT model combination that uses an HPMA-copolymer-methacryloyl-glycine-glycine-cephalosporin-doxorubicin (HPMA-co-MA-GG-C-Dox) as the macromolecular prodrug and an HPMA copolymer conjugate containing the nonmammalian enzyme beta-lactamase (HPMA-co-MA-GG-beta-L) as the activating component. HPMA-co-MA-GG-C-Dox had a molecular weight of approximately 31 600 Da and a C-Dox content of 5.85 wt %. Whereas free beta-L has a molecular weight of 45 kDa, the HPMA-co-MA-GG-beta-L conjugate had a molecular weight in the range of 75-150 kDa, and following purification no free enzyme was detectable. Against the cephalosporin C or HPMA-co-MA-GG-C-Dox substrates, the HPMA-co-MA-GG-beta-L conjugate retained 70% and 80% of its activity, respectively. In vivo (125)I-labeled HPMA-co-MA-GG-beta-L showed prolonged plasma concentration and greater tumor targeting than (125)I-labeled beta-L due to the enhanced permeability and retention (EPR) effect. Moreover, administration of HPMA-co-MA-GG-C-Dox iv to mice bearing sc B16F10 melanoma followed after 5 h by HPMA-co-MA-GG-beta-L led to release of free Dox. The PDEPT combination caused a significant decrease in tumor growth (T/C = 132%) whereas neither free Dox nor HPMA-co-MA-GG-C-Dox alone displayed activity. The PDEPT combination displayed no toxicity at the doses used, so further evaluation of this approach to establish the maximum tolerated dose (MTD) is recommended.

The 1-(2-nitrophenyl)thiosemicarbazide (TSC) derivative, (S)-1-[4-(4-benzhydrylthiosemicarbazido)-3-nitrobenzenesulfonyl]pyrrolidine-2-carboxylic acid [2-[(2-dimethylaminoethyl)methylamino]ethyl]amide (bradyzide; (S)-4), was recently disclosed as a novel, potent, orally active nonpeptide bradykinin (BK) B2 receptor antagonist. The compound inhibited the specific binding of [3H]BK to NG108-15 cell membrane preparations (rodent neuroblastoma-glioma) expressing B2 receptors with a K(i) of 0.5 +/- 0.2 nM. Compound (S)-4 also demonstrated oral efficacy against Freund's complete adjuvant (FCA)-induced mechanical hyperalgesia in rats with an ED50 value of 0.84 micromol/kg. After we optimized the terminal binding determinants projecting from the TSC framework, we found that it was possible to replace the potentially toxicophoric nitro and divalent sulfur moieties with only a 15-fold loss in binding affinity ((S)-14a). However, bradyzide and its congeners were found to have much lower affinities for cloned human B2 receptors, expressed in Cos-7 cells. The hitherto synthesized TSC series was screened against the human B2 receptor, and the dibenzosuberane (DBS) pharmacophore emerged as the key structural requirement for potency. Incorporation of this group resulted in a series of derivatives ((S)-14d,e and 19b-d) with K(i) ranges of 10.7-176 nM in NG108-15 cells (expressing the rodent B2 receptor) and 0.79-253 nM in Cos-7 cells (expressing the human B2 receptor). There was no evidence of agonist activity with any of the nonpeptides in any of the cell lines tested. In vivo, oral administration of compound 19c reversed FCA-induced and turpentine-induced mechanical hyperalgesia in rodents with ED50 values of 0.027 and 0.32 micromol/kg, respectively. The selectivity profiles of compounds (S)-14f and (S)-14g were also assessed to determine the conformational and/or steric preferences of the double-ring arrangement. The affinity of (S)-14 g for the human B2 receptor suggested that it may be a hydrophobic interaction with the ethane bridge of the DBS moiety that accounts for the increased potency of compounds (S)-14d,e and 19b,c at this receptor, by favoring a binding mode inaccessible to the unsubstituted diphenylmethyl derivative, (S)-4.