Publisher Correction: A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

Mass spectrometry-based discovery proteomics is an essential tool for the proximal readout of cellular drug action. Here, we apply a robust proteomic workflow to rapidly profile the proteomes of five lung cancer cell lines in response to more than 50 drugs. Integration of millions of quantitative protein-drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins that frequently responded to drugs and the aggregation of proteome changes across cell lines resolved compound effects on proteostasis. We leveraged these findings to demonstrate efficient target identification of chemical protein degraders. Aggregating drug response across cell lines also revealed that one-quarter of compounds modulated the abundance of one of their known protein targets. Finally, the proteomic data led us to discover that inhibition of mitochondrial function is an off-target mechanism of the MAP2K1/2 inhibitor PD184352 and that the ALK inhibitor ceritinib modulates autophagy.

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.

Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models?

Given a particular descriptor/method combination, some quantitative structure-activity relationship (QSAR) datasets are very predictive by random-split cross-validation while others are not. Recent literature in modelability suggests that the limiting issue for predictivity is in the data, not the QSAR methodology, and the limits are due to activity cliffs. Here, we investigate, on in-house data, the relative usefulness of experimental error, distribution of the activities, and activity cliff metrics in determining how predictive a dataset is likely to be. We include unmodified in-house datasets, datasets that should be perfectly predictive based only on the chemical structure, datasets where the distribution of activities is manipulated, and datasets that include a known amount of added noise. We find that activity cliff metrics determine predictivity better than the other metrics we investigated, whatever the type of dataset, consistent with the modelability literature. However, such metrics cannot distinguish real activity cliffs due to large uncertainties in the activities. We also show that a number of modern QSAR methods, and some alternative descriptors, are equally bad at predicting the activities of compounds on activity cliffs, consistent with the assumptions behind "modelability." Finally, we relate time-split predictivity with random-split predictivity and show that different coverages of chemical space are at least as important as uncertainty in activity and/or activity cliffs in limiting predictivity.

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.

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.

Construction of a 3D-shaped, natural product like fragment library by fragmentation and diversification of natural products.

A fragment library consisting of 3D-shaped, natural product-like fragments was assembled. Library construction was mainly performed by natural product degradation and natural product diversification reactions and was complemented by the identification of 3D-shaped, natural product like fragments available from commercial sources. In addition, during the course of these studies, novel rearrangements were discovered for Massarigenin C and Cytochalasin E. The obtained fragment library has an excellent 3D-shape and natural product likeness, covering a novel, unexplored and underrepresented chemical space in fragment based drug discovery (FBDD).

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.

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.

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.

Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules.

Virtual screening using bioactivity profiles has become an integral part of currently applied hit finding methods in pharmaceutical industry. However, a significant drawback of this approach is that it is only applicable to compounds that have been biologically tested in the past and have sufficient activity annotations for meaningful profile comparisons. Although bioactivity data generated in pharmaceutical institutions are growing on an unprecedented scale, the number of biologically annotated compounds still covers only a minuscule fraction of chemical space. For a newly synthesized compound or an isolated natural product to be biologically characterized across multiple assays, it may take a considerable amount of time. Consequently, this chemical matter will not be included in virtual screening campaigns based on bioactivity profiles. To overcome this problem, we herein introduce bioturbo similarity searching that uses chemical similarity to map molecules without biological annotations into bioactivity space and then searches for biologically similar compounds in this reference system. In benchmark calculations on primary screening data, we demonstrate that our approach generally achieves higher hit rates and identifies structurally more diverse compounds than approaches using chemical information only. Furthermore, our method is able to discover hits with novel modes of inhibition that traditional 2D and 3D similarity approaches are unlikely to discover. Test calculations on a set of natural products reveal the practical utility of the approach for identifying novel and synthetically more accessible chemical matter.

Chemotography for multi-target SAR analysis in the context of biological pathways.

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.

High-Throughput Screen for Inhibitors of Klebsiella pneumoniae Virulence Using a Tetrahymena pyriformis Co-Culture Surrogate Host Model.

The continuing emergence of antibacterial resistance reduces the effectiveness of antibiotics and drives an ongoing search for effective replacements. Screening compound libraries for antibacterial activity in standard growth media has been extensively explored and may be showing diminishing returns. Inhibition of bacterial targets that are selectively important under in vivo (infection) conditions and, therefore, would be missed by conventional in vitro screens might be an alternative. Surrogate host models of infection, however, are often not suitable for high-throughput screens. Here, we adapted a medium-throughput Tetrahymena pyriformis surrogate host model that was successfully used to identify inhibitors of a hyperviscous Klebsiella pneumoniae strain to a high-throughput format and screened circa 1.2 million compounds. The screen was robust and identified confirmed hits from different chemical classes with potent inhibition of K. pneumoniae growth in the presence of T. pyriformis that lacked any appreciable direct antibacterial activity. Several of these appeared to inhibit capsule/mucoidy, which are key virulence factors in hypervirulent K. pneumoniae. A weakly antibacterial inhibitor of LpxC (essential for the synthesis of the lipid A moiety of lipopolysaccharides) also appeared to be more active in the presence of T. pyriformis, which is consistent with the role of LPS in virulence as well as viability in K. pneumoniae.

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.

Identification of TNO155, an Allosteric SHP2 Inhibitor for the Treatment of Cancer.

SHP2 is a nonreceptor protein tyrosine phosphatase encoded by the PTPN11 gene and is involved in cell growth and differentiation via the MAPK signaling pathway. SHP2 also plays an important role in the programed cell death pathway (PD-1/PD-L1). As an oncoprotein as well as a potential immunomodulator, controlling SHP2 activity is of high therapeutic interest. As part of our comprehensive program targeting SHP2, we identified multiple allosteric binding modes of inhibition and optimized numerous chemical scaffolds in parallel. In this drug annotation report, we detail the identification and optimization of the pyrazine class of allosteric SHP2 inhibitors. Structure and property based drug design enabled the identification of protein-ligand interactions, potent cellular inhibition, control of physicochemical, pharmaceutical and selectivity properties, and potent in vivo antitumor activity. These studies culminated in the discovery of TNO155, (3S,4S)-8-(6-amino-5-((2-amino-3-chloropyridin-4-yl)thio)pyrazin-2-yl)-3-methyl-2-oxa-8-azaspiro[4.5]decan-4-amine (1), a highly potent, selective, orally efficacious, and first-in-class SHP2 inhibitor currently in clinical trials for cancer.

Plate-based diversity selection based on empirical HTS data to enhance the number of hits and their chemical diversity.

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.

Which aspects of HTS are empirically correlated with downstream success?

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.

"Virtual fragment linking": an approach to identify potent binders from low affinity fragment hits.

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.

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.

Dual Allosteric Inhibition of SHP2 Phosphatase.

SHP2 is a cytoplasmic protein tyrosine phosphatase encoded by the PTPN11 gene and is involved in cell proliferation, differentiation, and survival. Recently, we reported an allosteric mechanism of inhibition that stabilizes the auto-inhibited conformation of SHP2. SHP099 (1) was identified and characterized as a moderately potent, orally bioavailable, allosteric small molecule inhibitor, which binds to a tunnel-like pocket formed by the confluence of three domains of SHP2. In this report, we describe further screening strategies that enabled the identification of a second, distinct small molecule allosteric site. SHP244 (2) was identified as a weak inhibitor of SHP2 with modest thermal stabilization of the enzyme. X-ray crystallography revealed that 2 binds and stabilizes the inactive, closed conformation of SHP2, at a distinct, previously unexplored binding site-a cleft formed at the interface of the N-terminal SH2 and PTP domains. Derivatization of 2 using structure-based design resulted in an increase in SHP2 thermal stabilization, biochemical inhibition, and subsequent MAPK pathway modulation. Downregulation of DUSP6 mRNA, a downstream MAPK pathway marker, was observed in KYSE-520 cancer cells. Remarkably, simultaneous occupation of both allosteric sites by 1 and 2 was possible, as characterized by cooperative biochemical inhibition experiments and X-ray crystallography. Combining an allosteric site 1 inhibitor with an allosteric site 2 inhibitor led to enhanced pharmacological pathway inhibition in cells. This work illustrates a rare example of dual allosteric targeted protein inhibition, demonstrates screening methodology and tactics to identify allosteric inhibitors, and enables further interrogation of SHP2 in cancer and related pathologies.