PepSeA: Peptide Sequence Alignment and Visualization Tools to Enable Lead Optimization.

Therapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target "undruggable" proteins that are associated with a wide range of pathologies. Despite their importance, current molecular design capabilities that inform medicinal chemistry decisions on peptide programs are limited. More specifically, there are unmet needs for structure-activity relationship (SAR) analysis and visualization of linear, cyclic, and cross-linked peptides containing non-natural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA (Peptide Sequence Alignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multiple sequence alignment of non-natural amino acids and enhanced visualization with the hierarchical editing language for macromolecules (HELM). Via stepwise SAR analysis of a ChEMBL peptide data set, we demonstrate the utility of PepSeA to accelerate decision making in lead optimization campaigns in pharmaceutical setting. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design-make-test cycles.

Diversity & tractability revisited in collaborative small molecule phenotypic screening library design.

Identification of purposeful chemical matter on a broad range of drug targets is of high importance to the pharmaceutical industry. However, disease-relevant but more complex hit-finding plans require flexibility regarding the subset of the compounds that we screen. Herein we describe a strategy to design high-quality small molecule screening subsets of two different sizes to cope with a rapidly changing early discovery portfolio. The approach taken balances chemical tractability, chemical diversity and biological target coverage. Furthermore, using surveys, we actively involved chemists within our company in the selection process of the diversity decks to ensure current medicinal chemistry principles were incorporated. The chemist surveys revealed that not all published PAINS substructure alerts are considered productive by the medicinal chemistry community and in agreement with previously published results from other institutions, QED scores tracked quite well with chemists' notions of chemical attractiveness.

Reducing Limitation in Probe Design: The Development of a Diazirine-Compatible Suzuki-Miyaura Cross Coupling Reaction.

Access to high quality photoaffinity probe molecules is often constrained by synthetic limitations related to diazirine installation. A survey of recently published photoaffinity probe syntheses identified the Suzuki-Miyaura (S-M) coupling reaction, ubiquitous in drug discovery, as being underutilized to incorporate diazirines. To test whether advances in modern cross-coupling catalysis might enable efficient S-M couplings tolerant of the diazirine moiety, a fragment-based screening approach was employed. A model S-M coupling reaction was screened under various conditions in the presence of an aromatic diazirine fragment. This screen identified reaction conditions that gave good yields of S-M coupling product while minimally perturbing the diazirine reporter fragment. These conditions were found to be highly scalable and exhibited broad scope when applied to a chemistry informer library of 24 pharmaceutically relevant aryl boron pinacol esters. Furthermore, these conditions were used to synthesize a known diazirine-containing probe molecule with improved synthetic efficiency.

Plate-Based Phenotypic Screening for Pain Using Human iPSC-Derived Sensory Neurons.

Screening against a disease-relevant phenotype to identify compounds that change the outcome of biological pathways, rather than just the activity of specific targets, offers an alternative approach to find modulators of disease characteristics. However, in pain research, use of in vitro phenotypic screens has been impeded by the challenge of sourcing relevant neuronal cell types in sufficient quantity and developing functional end-point measurements with a direct disease link. To overcome these hurdles, we have generated human induced pluripotent stem cell (hiPSC)-derived sensory neurons at a robust production scale using the concept of cryopreserved "near-assay-ready" cells to decouple complex cell production from assay development and screening. hiPSC sensory neurons have then been used for development of a 384-well veratridine-evoked calcium flux assay. This functional assay of neuronal excitability was validated for phenotypic relevance to pain and other hyperexcitability disorders through screening a small targeted validation compound subset. A 2700-compound chemogenomics screen was then conducted to profile the range of target-based mechanisms able to inhibit veratridine-evoked excitability. This report presents the assay development, validation, and screening data. We conclude that high-throughput-compatible pain-relevant phenotypic screening with hiPSC sensory neurons is feasible and ready for application for the identification of new targets, pathways, mechanisms of action, and compounds for modulating neuronal excitability.

CHEMGENIE: integration of chemogenomics data for applications in chemical biology.

Increasing amounts of biological data are accumulating in the pharmaceutical industry and academic institutions. However, data does not equal actionable information, and guidelines for appropriate data capture, harmonization, integration, mining, and visualization need to be established to fully harness its potential. Here, we describe ongoing efforts at Merck & Co. to structure data in the area of chemogenomics. We are integrating complementary data from both internal and external data sources into one chemogenomics database (Chemical Genetic Interaction Enterprise; CHEMGENIE). Here, we demonstrate how this well-curated database facilitates compound set design, tool compound selection, target deconvolution in phenotypic screening, and predictive model building.

The Identification of Pivotal Transcriptional Factors Mediating Cell Responses to Drugs With Drug-Induced Liver Injury Liabilities.

Drug-induced liver injury (DILI) is a leading cause of drug attrition during drug development and a common reason for drug withdrawal from the market. The poor predictability of conventional animal-based approaches necessitates the development of alternative testing approaches. A body of evidence associates DILI with the induction of stress-response genes in liver cells. Here, we set out to identify signal transduction pathways predominantly involved in the regulation of gene transcription by DILI drugs. To this end, we employed ATTAGENE's cell-based multiplexed reporter assay, the FACTORIAL transcription factor (TF), that enables quantitative assessment of the activity of multiple stress-responsive TFs in a single well of cells. Homogeneous reporter system enables quantitative functional assessment of multiple transcription factors. Nat. Methods 5, 253-260). Using this assay, we assessed TF responses of the human hepatoma cell line HepG2 to a panel of 64 drug candidates, including 23 preclinical DILI and 11 clinical DILI compounds and 30 nonhepatotoxic compounds from a diverse physicochemical property space. We have identified 16 TF families that specifically responded to DILI drugs, including nuclear factor (erythroid-derived 2)-like 2 antioxidant response element, octamer, hypoxia inducible factor 1 alpha, farnesoid-X receptor, TCF/beta-catenin, aryl hydrocarbon receptor, activator protein-1, E2F, early growth response-1, metal-response transcription factor 1, sterol regulatory element-binding protein, paired box protein, peroxisome proliferator-activated receptor, liver X receptor, interferone regulating factor, and P53, and 2 promoters that responded to multiple TFs (cytomegalovirus and direct repeat 3/vitamin D receptor). Some of TFs identified here also have previously defined role in pathogenesis of liver diseases. These data demonstrate the utility of cost-effective, animal-free, TF profiling assay for detecting DILI potential of drug candidates at early stages of drug development.

Deorphanization strategies for dark chemical matter.

The term dark chemical matter (DCM) was recently introduced for those molecules in a screening collection that have never shown any substantial biological activity despite having been tested in hundreds of high-throughput assays. It was suggested that, if hits emerge from this compound pool in future screening campaigns, they should be prioritized due to their exquisite selectivity profile. In this article we define DCM at our company and describe on-going efforts to shed light on the bioactivity of these apparently silent compounds, with an emphasis on multi-parametric profiling methods. It is also demonstrated that compounds that are dark within one institution might be found active in another, but typically show the foretold selectivity.

Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score.

High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.

Amine promiscuity and toxicology analysis.

Drug discovery programs often face challenges to obtain sufficient duration of action of the drug (i.e. seek longer half-lives). If the pharmacodynamic response is driven by free plasma concentration of the drug then extending the plasma drug concentration is a valid approach. Half-life is dependent on the volume of distribution, which in turn can be dependent upon the ionization state of the molecule. Basic compounds tend to have a higher volume of distribution leading to longer half-lives. However, it has been shown that bases may also have higher promiscuity. In this work, we describe an analysis of in vitro pharmacological profiling and toxicology data investigating the role of primary, secondary, and tertiary amines in imparting promiscuity and thus off-target toxicity. Primary amines are found to be less promiscuous in in vitro assays and have improved profiles in in vivo toxicology studies compared to secondary and tertiary amines.

Public Domain HTS Fingerprints: Design and Evaluation of Compound Bioactivity Profiles from PubChem's Bioassay Repository.

Molecular profiling efforts aim at characterizing the biological actions of small molecules by screening them in hundreds of different biochemical and/or cell-based assays. Together, these assays yield a rich data landscape of target-based and phenotypic effects of the tested compounds. However, submitting an entire compound library to a molecular profiling panel can easily become cost-prohibitive. Here, we make use of historical screening assays to create comprehensive bioactivity profiles for more than 300 000 small molecules. These bioactivity profiles, termed PubChem high-throughput screening fingerprints (PubChem HTSFPs), report small molecule activities in 243 different PubChem bioassays. Although the assays originate from originally independently pursued drug or probe discovery projects, we demonstrate their value as molecular signatures when used in combination. We use these PubChem HTSFPs as molecular descriptors in hit expansion experiments for 33 different targets and phenotypes, showing that, on average, they lead to 27 times as many hits in a set of 1000 chosen molecules as a random screening subset of the same size (average ROC score: 0.82). Moreover, we demonstrate that PubChem HTSFPs retrieve hits that are structurally diverse and distinct from active compounds retrieved by chemical similarity-based hit expansion methods. PubChem HTSFPs are made freely available for the chemical biology research community.

Dark chemical matter as a promising starting point for drug lead discovery.

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.

Experimental design strategy: weak reinforcement leads to increased hit rates and enhanced chemical diversity.

High Throughput Screening (HTS) is a common approach in life sciences to discover chemical matter that modulates a biological target or phenotype. However, low assay throughput, reagents cost, or a flowchart that can deal with only a limited number of hits may impair screening large numbers of compounds. In this case, a subset of compounds is assayed, and in silico models are utilized to aid in iterative screening design, usually to expand around the found hits and enrich subsequent rounds for relevant chemical matter. However, this may lead to an overly narrow focus, and the diversity of compounds sampled in subsequent iterations may suffer. Active learning has been recently successfully applied in drug discovery with the goal of sampling diverse chemical space to improve model performance. Here we introduce a robust and straightforward iterative screening protocol based on naïve Bayes models. Instead of following up on the compounds with the highest scores in the in silico model, we pursue compounds with very low but positive values. This includes unique chemotypes of weakly active compounds that enhance the applicability domain of the model and increase the cumulative hit rates. We show in a retrospective application to 81 Novartis assays that this protocol leads to consistently higher compound and scaffold hit rates compared to a standard expansion around hits or an active learning approach. We recommend using the weak reinforcement strategy introduced herein for iterative screening workflows.

Large scale meta-analysis of fragment-based screening campaigns: privileged fragments and complementary technologies.

A first step in fragment-based drug discovery (FBDD) often entails a fragment-based screen (FBS) to identify fragment "hits." However, the integration of conflicting results from orthogonal screens remains a challenge. Here we present a meta-analysis of 35 fragment-based campaigns at Novartis, which employed a generic 1400-fragment library against diverse target families using various biophysical and biochemical techniques. By statistically interrogating the multidimensional FBS data, we sought to investigate three questions: (1) What makes a fragment amenable for FBS? (2) How do hits from different fragment screening technologies and target classes compare with each other? (3) What is the best way to pair FBS assay technologies? In doing so, we identified substructures that were privileged for specific target classes, as well as fragments that were privileged for authentic activity against many targets. We also revealed some of the discrepancies between technologies. Finally, we uncovered a simple rule of thumb in screening strategy: when choosing two technologies for a campaign, pairing a biochemical and biophysical screen tends to yield the greatest coverage of authentic hits.

The opportunities of mining historical and collective data in drug discovery.

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.

Composition and applications of focus libraries to phenotypic assays.

The wealth of bioactivity information now available on low-molecular weight compounds has enabled a paradigm shift in chemical biology and early phase drug discovery efforts. Traditionally chemical libraries have been most commonly employed in screening approaches where a bioassay is used to characterize a chemical library in a random search for active samples. However, robust curating of bioassay data, establishment of ontologies enabling mining of large chemical biology datasets, and a wealth of public chemical biology information has made possible the establishment of highly annotated compound collections. Such annotated chemical libraries can now be used to build a pathway/target hypothesis and have led to a new view where chemical libraries are used to characterize a bioassay. In this article we discuss the types of compounds in these annotated libraries composed of tools, probes, and drugs. As well, we provide rationale and a few examples for how such libraries can enable phenotypic/forward chemical genomic approaches. As with any approach, there are several pitfalls that need to be considered and we also outline some strategies to avoid these.

A screening pattern recognition method finds new and divergent targets for drugs and natural products.

Computational target prediction methods using chemical descriptors have been applied exhaustively in drug discovery to elucidate the mechanisms-of-action (MOAs) of small molecules. To predict truly novel and unexpected small molecule-target interactions, compounds must be compared by means other than their chemical structure alone. Here we investigated predictions made by a method, HTS fingerprints (HTSFPs), that matches patterns of activities in experimental screens. Over 1,400 drugs and 1,300 natural products (NPs) were screened in more than 200 diverse assays, creating encodable activity patterns. The comparison of these activity patterns to an MOA-annotated reference panel led to the prediction of 5,281 and 2,798 previously unknown targets for the NP and drug sets, respectively. Intriguingly, there was limited overlap among the targets predicted; the drugs were more biased toward membrane receptors and the NPs toward soluble enzymes, consistent with the idea that they represent unexplored pharmacologies. Importantly, HTSFPs inferred targets that were beyond the prediction capabilities of standard chemical descriptors, especially for NPs but also for the more explored drug set. Of 65 drug-target predictions that we tested in vitro, 48 (73.8%) were confirmed with AC50 values ranging from 38 nM to 29 µM. Among these interactions was the inhibition of cyclooxygenases 1 and 2 by the HIV protease inhibitor Tipranavir. These newly discovered targets that are phylogenetically and phylochemically distant to the primary target provide an explanation for spontaneous bleeding events observed for patients treated with this drug, a physiological effect that was previously difficult to reconcile with the drug's known MOA.

ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery.

Large scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms.

Efficient search of chemical space: navigating from fragments to structurally diverse chemotypes.

We introduce a novel strategy to sample bioactive chemical space, which follows-up on hits from fragment campaigns without the need for a crystal structure. Our results strongly suggest that screening a few hundred or thousand fragments can substantially improve the selection of small-molecule screening subsets. By combining fragment-based screening with virtual fragment linking and HTS fingerprints, we have developed an effective strategy not only to expand from low-affinity hits to potent compounds but also to hop in chemical space to substantially novel chemotypes. In benchmark calculations, our approach accessed subsets of compounds that were substantially enriched in chemically diverse hit compounds for various activity classes. Overall, half of the hits in the screening collection were found by screening only 10% of the library. Furthermore, a prospective application led to the discovery of two structurally novel histone deacetylase 4 inhibitors.

Entourage: visualizing relationships between biological pathways using contextual subsets.

Biological pathway maps are highly relevant tools for many tasks in molecular biology. They reduce the complexity of the overall biological network by partitioning it into smaller manageable parts. While this reduction of complexity is their biggest strength, it is, at the same time, their biggest weakness. By removing what is deemed not important for the primary function of the pathway, biologists lose the ability to follow and understand cross-talks between pathways. Considering these cross-talks is, however, critical in many analysis scenarios, such as judging effects of drugs. In this paper we introduce Entourage, a novel visualization technique that provides contextual information lost due to the artificial partitioning of the biological network, but at the same time limits the presented information to what is relevant to the analyst's task. We use one pathway map as the focus of an analysis and allow a larger set of contextual pathways. For these context pathways we only show the contextual subsets, i.e., the parts of the graph that are relevant to a selection. Entourage suggests related pathways based on similarities and highlights parts of a pathway that are interesting in terms of mapped experimental data. We visualize interdependencies between pathways using stubs of visual links, which we found effective yet not obtrusive. By combining this approach with visualization of experimental data, we can provide domain experts with a highly valuable tool. We demonstrate the utility of Entourage with case studies conducted with a biochemist who researches the effects of drugs on pathways. We show that the technique is well suited to investigate interdependencies between pathways and to analyze, understand, and predict the effect that drugs have on different cell types.