Cheminformatics aspects of high throughput screening: from robots to models: symposium summary

The “Cheminformatics aspects of high throughput screening (HTS): from robots to models” symposium was part of the computers in chemistry technical program at the American Chemical Society National Meeting in Denver, Colorado during the fall of 2011. This symposium brought together researchers from high throughput screening centers and molecular modelers from academia and industry to discuss the integration of currently available high throughput screening data and assays with computational analysis. The topics discussed at this symposium covered the data-infrastructure at various academic, hospital, and National Institutes of Health-funded high throughput screening centers, the cheminformatics and molecular modeling methods used in real world examples to guide screening and hit-finding, and how academic and non-profit organizations can benefit from current high throughput screening cheminformatics resources. Specifically, this article also covers the remarks and discussions in the open panel discussion of the symposium and summarizes the following talks on “Accurate Kinase virtual screening: biochemical, cellular and selectivity”, “Selective, privileged and promiscuous chemical patterns in high-throughput screening” and “Visualizing and exploring relationships among HTS hits using network graphs”.     

Integrity profiling of high throughput screening hits using LC-MS and related techniques

Integrity profiling of HTS hits is valuable for verification of the hit identity and purity. This provides early discovery researchers with more confident SAR theories. Methodology for integrity profiling of HTS hits must be high throughput, consume little material, and selectively provide structure-based data. Analytical techniques that can be utilized for integrity profiling methods are reviewed for their appropriateness in sample preparation, component separation, detection, purity quantitation, identity confirmation, and follow-up.     

Activity-aware clustering of high throughput screening data and elucidation of orthogonal structure-activity relationships

From a medicinal chemistry point of view, one of the primary goals of high throughput screening (HTS) hit list assessment is the identification of chemotypes with an informative structure-activity relationship (SAR). Such chemotypes may enable optimization of the primary potency, as well as selectivity and phamacokinetic properties. A common way to prioritize them is molecular clustering of the hits. Typical clustering techniques, however, rely on a general notion of chemical similarity or standard rules of scaffold decomposition and are thus insensitive to molecular features that are enriched in biologically active compounds. This hinders SAR analysis, because compounds sharing the same pharmacophore might not end up in the same cluster and thus are not directly compared to each other by the medicinal chemist. Similarly, common chemotypes that are not related to activity may contaminate clusters, distracting from important chemical motifs. We combined molecular similarity and Bayesian models and introduce (I) a robust, activity-aware clustering approach and (II) a feature mapping method for the elucidation of distinct SAR determinants in polypharmacologic compounds. We evaluated the method on 462 dose-response assays from the Pubchem Bioassay repository. Activity-aware clustering grouped compounds sharing molecular cores that were specific for the target or pathway at hand, rather than grouping inactive scaffolds commonly found in compound series. Many of these core structures we also found in literature that discussed SARs of the respective targets. A numerical comparison of cores allowed for identification of the structural prerequisites for polypharmacology, i.e., distinct bioactive regions within a single compound, and pointed toward selectivity-conferring medchem strategies. The method presented here is generally applicable to any type of activity data and may help bridge the gap between hit list assessment and designing a medchem strategy.     

ALARM NMR: a rapid and robust experimental method to detect reactive false positives in biochemical screens

High-throughput screening (HTS) of large compound collections typically results in numerous small molecule hits that must be carefully evaluated to identify valid drug leads. Although several filtering mechanisms and other tools exist that can assist the chemist in this process, it is often the case that costly synthetic resources are expended in pursuing false positives. We report here a rapid and reliable NMR-based method for identifying reactive false positives including those that oxidize or alkylate a protein target. Importantly, the reactive species need not be the parent compound, as both reactive impurities and breakdown products can be detected. The assay is called ALARM NMR (a La assay to detect reactive molecules by nuclear magnetic resonance) and is based on monitoring DTT-dependent (13)C chemical shift changes of the human La antigen in the presence of a test compound or mixture. Extensive validation has been performed to demonstrate the reliability and utility of using ALARM NMR to assess thiol reactivity. This included comparing ALARM NMR to a glutathione-based fluorescence assay, as well as testing a collection of more than 3500 compounds containing HTS hits from 23 drug targets. The data show that current in silico filtering tools fail to identify more than half of the compounds that can act via reactive mechanisms. Significantly, we show how ALARM NMR data has been critical in identifying reactive compounds that would otherwise have been prioritized for lead optimization. In addition, a new filtering tool has been developed on the basis of the ALARM NMR data that can augment current in silico programs for identifying nuisance compounds and improving the process of hit triage.     

Demonstration of the utility of DOS-derived fragment libraries for rapid hit derivatisation in a multidirectional fashion

Organic synthesis underpins the evolution of weak fragment hits into potent lead compounds. Deficiencies within current screening collections often result in the requirement of significant synthetic investment to enable multidirectional fragment growth, limiting the efficiency of the hit evolution process. Diversity-oriented synthesis (DOS)-derived fragment libraries are constructed in an efficient and modular fashion and thus are well-suited to address this challenge. To demonstrate the effective nature of such libraries within fragment-based drug discovery, we herein describe the screening of a 40-member DOS library against three functionally distinct biological targets using X-Ray crystallography. Firstly, we demonstrate the importance for diversity in aiding hit identification with four fragment binders resulting from these efforts. Moreover, we also exemplify the ability to readily access a library of analogues from cheap commercially available materials, which ultimately enabled the exploration of a minimum of four synthetic vectors from each molecule. In total, 10–14 analogues of each hit were rapidly accessed in three to six synthetic steps. Thus, we showcase how DOS-derived fragment libraries enable efficient hit derivatisation and can be utilised to remove the synthetic limitations encountered in early stage fragment-based drug discovery.

Fragment-based screening of a shape-diverse collection yielded four hits against three proteins. Up to 14 analogues of each hit were rapidly generated, enabling four fragment growth vectors to be explored using inexpensive materials and reliable synthetic transformations.     

Novel statistical approach for primary high-throughput screening hit selection

The standard activity threshold-based method (the "top X" approach), currently widely used in the high-throughput screening (HTS) data analysis, is ineffective at identifying good-quality hits. We have proposed a novel knowledge-based statistical approach, driven by the hidden structure-activity relationship (SAR) within a screening library, for primary hit selection. Application to an in-house ultrahigh-throughput screening (uHTS) campaign has demonstrated it can directly identify active scaffolds containing valuable SAR information with a greatly improved confirmation rate compared to the standard "top X" method (from 55% to 85%). This approach may help produce high-quality leads and expedite the hit-to-lead process in drug discovery.     

How large does a compound screening collection need to be?

Increasingly, chemical libraries are being produced which are focused on a biological target or group of related targets, rather than simply being constructed in a combinatorial fashion. A screening collection compiled from such libraries will contain multiple analogues of a number of discrete series of compounds. The question arises as to how many analogues are necessary to represent each series in order to ensure that an active series will be identified. Based on a simple probabilistic argument and supported by in-house screening data, guidelines are given for the number of compounds necessary to achieve a "hit", or series of hits, at various levels of certainty. Obtaining more than one hit from the same series is useful since this gives early acquisition of SAR (structure-activity relationship) and confirms a hit is not a singleton. We show that screening collections composed of only small numbers of analogues of each series are sub-optimal for SAR acquisition. Based on these studies, we recommend a minimum series size of about 200 compounds. This gives a high probability of confirmatory SAR (i.e. at least two hits from the same series). More substantial early SAR (at least 5 hits from the same series) can be gained by using series of about 650 compounds each. With this level of information being generated, more accurate assessment of the likely success of the series in hit-to-lead and later stage development becomes possible.     

A collaborative hit-to-lead investigation leveraging medicinal chemistry expertise with high throughput library design, synthesis and purification capabilities

High throughput screening (HTS) campaigns, where laboratory automation is used to expose biological targets to large numbers of materials from corporate compound collections, have become commonplace within the lead generation phase of pharmaceutical discovery. Advances in genomics and related fields have afforded a wealth of targets such that screening facilities at larger organizations routinely execute over 100 hit-finding campaigns per year. Often, 10(5) or 10(6) molecules will be tested within a campaign/cycle to locate a large number of actives requiring follow-up investigation. Due to resource constraints at every organization, traditional chemistry methods for validating hits and developing structure activity relationships (SAR) become untenable when challenged with hundreds of hits in multiple chemical families per target. To compound the issue, comparison and prioritization of hits versus multiple screens, or physical chemical property criteria, is made more complex by the informatics issues associated with handling large data sets. This article describes a collaborative research project designed to simultaneously leverage the medicinal chemistry and drug development expertise of the Novartis Institutes for Biomedical Research Inc. (NIBRI) and ArQule Inc.'s high throughput library design, synthesis and purification capabilities. The work processes developed by the team to efficiently design, prepare, purify, assess and prioritize multiple chemical classes that were identified during high throughput screening, cheminformatics and molecular modeling activities will be detailed.     

An empirical process for the design of high-throughput screening deck filters

A process for objective identification and filtering of undesirable compounds that contribute to high-throughput screening (HTS) deck promiscuity is described. Two methods of mapping hit promiscuity have been developed linking SMARTS-based structural queries with historical primary HTS data. The first compares an expected assay hit rate to actual hit rates. The second examines the propensity of an individual compound to hit multiple assays. Statistical evaluation of the data indicates a correlation between the resultant functional group filters and compound promiscuity. These data corroborate a number of commonly applied filters as well as producing some unexpected results. Application of these models to HTS collection triage reduced the number of in-house compounds considered for screening by 12%. The implications of these findings are further discussed in the context of the HTS screening set and combinatorial library design as well as compound acquisition.     

Structure-based screening as applied to human FABP4: a highly efficient alternative to HTS for hit generation

The time-limiting step in HTS often is the development of an appropriate assay. In addition, hits from HTS fairly often turn out to be false positives and generally display unfavorable properties for further development. Here we describe an alternative process for hit generation, applied to the human adipocyte fatty acid binding protein FABP4. A small molecular ligand for FABP4 that blocks the binding of endogenous ligands may be developed into a drug for the treatment of type-2 diabetes. Using NMR spectroscopy, we screened FABP4 for low-affinity binders in a diversity library consisting of small soluble scaffolds, which yielded 52 initial hits in total. The potencies of these hits were ranked, and crystal structures of FABP4 complexes for two of the hits were obtained. The structural data were subsequently used to direct similarity searches for available analogues, as well as chemical synthesis of 12 novel analogues. In this way, a series of three selective FABP4 ligands with attractive pharmacochemical profiles and potencies of 10 microM or better was obtained.     

Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups

Non-specific chemical modification of protein thiol groups continues to be a significant source of false positive hits from high-throughput screening campaigns and can even plague certain protein targets and chemical series well into lead optimization. While experimental tools exist to assess the risk and promiscuity associated with the chemical reactivity of existing compounds, computational tools are desired that can reliably identify substructures that are associated with chemical reactivity to aid in triage of HTS hit lists, external compound purchases, and library design. Here we describe a Bayesian classification model derived from more than 8,800 compounds that have been experimentally assessed for their potential to covalently modify protein targets. The resulting model can be implemented in the large-scale assessment of compound libraries for purchase or design. In addition, the individual substructures identified as highly reactive in the model can be used as look-up tables to guide chemists during hit-to-lead and lead optimization campaigns.     

Understanding false positives in reporter gene assays: in silico chemogenomics approaches to prioritize cell-based HTS data

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.     

Biosensor-based small molecule fragment screening with biolayer interferometry

Biosensor-based fragment screening is a valuable tool in the drug discovery process. This method is advantageous over many biochemical methods because primary hits can be distinguished from non-specific or non-ideal interactions by examining binding profiles and responses, resulting in reduced false-positive rates. Biolayer interferometry (BLI), a technique that measures changes in an interference pattern generated from visible light reflected from an optical layer and a biolayer containing proteins of interest, is a relatively new method for monitoring small molecule interactions. The BLI format is based on a disposable sensor that is immersed in 96-well or 384-well plates. BLI has been validated for small molecule detection and fragment screening with model systems and well-characterized targets where affinity constants and binding profiles are generally similar to those obtained with surface plasmon resonsance (SPR). Screens with challenging targets involved in protein–protein interactions including BCL-2, JNK1, and eIF4E were performed with a fragment library of 6,500 compounds, and hit rates were compared for these targets. For eIF4E, a protein containing a PPI site and a nucleotide binding site, results from a BLI fragment screen were compared to results obtained in biochemical HTS screens. Overlapping hits were observed for the PPI site, and hits unique to the BLI screen were identified. Hit assessments with SPR and BLI are described.     

A HTS Assay for the Detection of Organophosphorus Nerve Agent Scavengers

A new pro‐fluorescent probe aimed at a HTS assay of scavengers is able to selectively and efficiently cleave the P? S bond of organophosphorus nerve agents and by this provides non‐toxic phosphonic acid has been designed and synthesised. The previously described pro‐fluorescent probes were based on a conventional activated P? Oaryl bond cleavage, whereas our approach uses a self‐immolative linker strategy that allows the detection of phosphonothioase activity with respect to a non‐activated P? Salkyl bond. Further, we have also developed and optimised a high‐throughput screening assay for the selection of decontaminants (chemical or biochemical scavengers) that could efficiently hydrolyse highly toxic V ‐type nerve agents. A preliminary screening, realised on a small α‐nucleophile library, allowed us to identify some preliminary “hits”, among which pyridinealdoximes, α‐oxo oximes, hydroxamic acids and, less active but more original, amidoximes were the most promising. Their selective phosphonothioase activity has been further confirmed by using PhX as the substrate, and thus they offer new perspectives for the synthesis of more potent V nerve agent scavengers.     

Novel isoquinolone PDK1 inhibitors discovered through fragment-based lead discovery

Phosphoinositide-dependent kinase-1 (PDK1) is a critical enzyme in the PI3K/AKT pathway and to the activation of AGC family protein kinases, including S6K, SGK, and PKC. Dysregulation of this pathway plays a key role in cancer cell growth, survival and tumor angiogenesis. As such, inhibitors of PDK1 offer the promise of a new therapeutic modality for cancer treatment. Fragment based drug screening has recently become a viable entry point for hit identification. In this work, NMR spectroscopy fragment screening of PDK1 afforded novel chemotypes as orthogonal starting points from HTS screening hits. Compounds identified as hits by NMR spectroscopy were tested in a biochemical assay, and fragments with activity in both assays were clustered. The Pfizer compound file was mined via substructure and 2D similarity search, and the chemotypes were prioritized by ligand efficiency (LE), SAR mining, chemical attractiveness, and chemical enablement of promising vectors. From this effort, an isoquinolone fragment hit, 5 (IC₅₀ 870 μM, LE = 0.39), was identified as a novel, ligand efficient inhibitor of PDK1 and a suitable scaffold for further optimization. Initially in the absence of crystallographic data, a fragment growing approach efficiently explored four vectors of the isoquinolone scaffold via parallel synthesis to afford a compound with crystallographic data, 16 (IC₅₀ 41.4 μM, LE = 0.33). Subsequent lead optimization efforts provided 24 (IC₅₀ 1.8 μM, LE = 0.42), with greater than fivefold selectivity against other key pathway kinases.     

Confirmation of primary active substances from high throughput screening of chemical and biological populations: a statistical approach and practical considerations

Many biologically important substances are discovered through screening of relevant chemical or biological libraries. The ability to find the active substances ("hits") from any random collection is largely determined by the quality of the assay and screening conditions. When a large population is screened for a specific characteristic, each member of that population is usually tested only once. Errors in the measurements require additional follow-up tests to confirm that each hit from the primary screen is truly active. In this report, we present a statistical model system that predicts the reliability of hits from a primary test as affected by the error in the assay and the choice of the hit threshold (hit limit). The hit confirmation rate, as well as false positive (representing substances that initially fall above the hit limit but whose true activity are below the hit limit) and false negative (representing substances that initially fall below the hit limit but whose true activity are in fact greater than the hit limit) rates have been analyzed with this model by computational simulation. This model can also be used in screen validation and post-screening data analysis. The statistical analysis presented here has broad implications and is applicable to screening of any large population for any specific characteristic. Obvious applications include drug discovery, gene chip analysis, population biology, directed molecular evolution, biological panning, and combinatorial material sciences.     

NIR-mbc94, a fluorescent ligand that binds to endogenous CB(2) receptors and is amenable to high-throughput screening

High-throughput screening (HTS) of chemical libraries is often used for the unbiased identification of compounds interacting with G protein-coupled receptors (GPCRs), the largest family of therapeutic targets. However, current HTS methods require removing GPCRs from their native environment, which modifies their pharmacodynamic properties and biases the screen toward false positive hits. Here, we developed and validated a molecular imaging (MI) agent, NIR-mbc94, which emits near infrared (NIR) light and selectively binds to endogenously expressed cannabinoid CB(2) receptors,?a recognized target for treating autoimmune diseases, chronic pain and cancer. The precision and ease of this assay allows for the HTS of compounds interacting with CB(2) receptors expressed in their native environment.     

3D cell culture models and organ-on-a-chip: Meet separation science and mass spectrometry

In vitro derived simplified 3D representations of human organs or organ functionalities are predicted to play a major role in disease modeling, drug development, and personalized medicine, as they complement traditional cell line approaches and animal models. The cells for 3D organ representations may be derived from primary tissues, embryonic stem cells or induced pluripotent stem cells and come in a variety of formats from aggregates of individual or mixed cell types, self-organizing in vitro developed “organoids” and tissue mimicking chips. Microfluidic devices that allow long-term maintenance and combination with other tissues, cells or organoids are commonly referred to as “microphysiological” or “organ-on-a-chip” systems. Organ-on-a-chip technology allows a broad range of “on-chip” and “off-chip” analytical techniques, whereby “on-chip” techniques offer the possibility of real time tracking and analysis. In the rapidly expanding tool kit for real time analytical assays, mass spectrometry, combined with “on-chip” electrophoresis, and other separation approaches offer attractive emerging tools. In this review, we provide an overview of current 3D cell culture models, a compendium of current analytical strategies, and we make a case for new approaches for integrating separation science and mass spectrometry in this rapidly expanding research field.     

Identification of potential Gly/NMDA receptor antagonists by cheminformatics approach: a combination of pharmacophore modelling,virtual screening and molecular docking studies

The Gly/NMDA receptor has become known as potential target for the management of neurodegenerative diseases. Discovery of Gly/NMDA antagonists has thus attracted much attention in recent years. In the present research, a cheminformatics approach has been used to determine structural requirements for Gly/NMDA antagonism and to identify potential antagonists. Here, 37 quinoxaline derivatives were selected to develop a significant pharmacophore model with good certainty. The selected model was validated by leave-one-out cross-validation, an external test set, decoy set and Y-randomization test. Applicability domain was verified by the standardization approach. The validated 3D-QSAR model was used to screen virtual hits from the ZINC database by pharmacophore mapping. Molecular docking was used for assessment of receptor–ligand binding modes and binding affinities. The GlideScore and molecular interactions with critical amino acids were considered as crucial features to identify final hits. Furthermore, hits were analysed for in silico pharmacokinetic parameters and Lipinski’s rule of five, demonstrating their potential as drug-like candidates. The PubChem and SciFinder search tools were used to authenticate the novelty of leads retrieved. Finally, five different leads have been suggested as putative novel candidates for the exploration of potent Gly/NMDA receptor antagonists.