Enhancing the Small-Scale Screenable Biological Space beyond Known Chemogenomics Libraries with Gray Chemical Matter─Compounds with Novel Mechanisms from High-Throughput Screening Profiles.

Phenotypic assays have become an established approach to drug discovery. Greater disease relevance is often achieved through cellular models with increased complexity and more detailed readouts, such as gene expression or advanced imaging. However, the intricate nature and cost of these assays impose limitations on their screening capacity, often restricting screens to well-characterized small compound sets such as chemogenomics libraries. Here, we outline a cheminformatics approach to identify a small set of compounds with likely novel mechanisms of action (MoAs), expanding the MoA search space for throughput limited phenotypic assays. Our approach is based on mining existing large-scale, phenotypic high-throughput screening (HTS) data. It enables the identification of chemotypes that exhibit selectivity across multiple cell-based assays, which are characterized by persistent and broad structure activity relationships (SAR). We validate the effectiveness of our approach in broad cellular profiling assays (Cell Painting, DRUG-seq, and Promotor Signature Profiling) and chemical proteomics experiments. These experiments revealed that the compounds behave similarly to known chemogenetic libraries, but with a notable bias toward novel protein targets. To foster collaboration and advance research in this area, we have curated a public set of such compounds based on the PubChem BioAssay dataset and made it available for use by the scientific community.

Screening in serum-derived medium reveals differential response to compounds targeting metabolism.

A challenge for screening new anticancer drugs is that efficacy in cell culture models is not always predictive of efficacy in patients. One limitation of standard cell culture is a reliance on non-physiological nutrient levels, which can influence cell metabolism and drug sensitivity. A general assessment of how physiological nutrients affect cancer cell response to small molecule therapies is lacking. To address this, we developed a serum-derived culture medium that supports the proliferation of diverse cancer cell lines and is amenable to high-throughput screening. We screened several small molecule libraries and found that compounds targeting metabolic enzymes were differentially effective in standard compared to serum-derived medium. We exploited the differences in nutrient levels between each medium to understand why medium conditions affected the response of cells to some compounds, illustrating how this approach can be used to screen potential therapeutics and understand how their efficacy is modified by available nutrients.

Screening in serum-derived medium reveals differential response to compounds targeting metabolism.

A challenge for screening new candidate drugs to treat cancer is that efficacy in cell culture models is not always predictive of efficacy in patients. One limitation of standard cell culture is a reliance on non-physiological nutrient levels to propagate cells. Which nutrients are available can influence how cancer cells use metabolism to proliferate and impact sensitivity to some drugs, but a general assessment of how physiological nutrients affect cancer cell response to small molecule therapies is lacking. To enable screening of compounds to determine how the nutrient environment impacts drug efficacy, we developed a serum-derived culture medium that supports the proliferation of diverse cancer cell lines and is amenable to high-throughput screening. We used this system to screen several small molecule libraries and found that compounds targeting metabolic enzymes were enriched as having differential efficacy in standard compared to serum-derived medium. We exploited the differences in nutrient levels between each medium to understand why medium conditions affected the response of cells to some compounds, illustrating how this approach can be used to screen potential therapeutics and understand how their efficacy is modified by available nutrients.

Tyrosine kinase inhibitors can activate the NLRP3 inflammasome in myeloid cells through lysosomal damage and cell lysis.

Inflammasomes are intracellular protein complexes that promote an inflammatory host defense in response to pathogens and damaged or neoplastic tissues and are implicated in inflammatory disorders and therapeutic-induced toxicity. We investigated the mechanisms of activation for inflammasomes nucleated by NOD-like receptor (NLR) protiens. A screen of a small-molecule library revealed that several tyrosine kinase inhibitors (TKIs)-including those that are clinically approved (such as imatinib and crizotinib) or are in clinical trials (such as masitinib)-activated the NLRP3 inflammasome. Furthermore, imatinib and masitinib caused lysosomal swelling and damage independently of their kinase target, leading to cathepsin-mediated destabilization of myeloid cell membranes and, ultimately, cell lysis that was accompanied by potassium (K+) efflux, which activated NLRP3. This effect was specific to primary myeloid cells (such as peripheral blood mononuclear cells and mouse bone marrow-derived dendritic cells) and did not occur in other primary cell types or various cell lines. TKI-induced lytic cell death and NLRP3 activation, but not lysosomal damage, were prevented by stabilizing cell membranes. Our findings reveal a potential immunological off-target of some TKIs that may contribute to their clinical efficacy or to their adverse effects.

DRUG-seq Provides Unbiased Biological Activity Readouts for Neuroscience Drug Discovery.

Unbiased transcriptomic RNA-seq data has provided deep insights into biological processes. However, its impact in drug discovery has been narrow given high costs and low throughput. Proof-of-concept studies with Digital RNA with pertUrbation of Genes (DRUG)-seq demonstrated the potential to address this gap. We extended the DRUG-seq platform by subjecting it to rigorous testing and by adding an open-source analysis pipeline. The results demonstrate high reproducibility and ability to resolve the mechanism(s) of action for a diverse set of compounds. Furthermore, we demonstrate how this data can be incorporated into a drug discovery project aiming to develop therapeutics for schizophrenia using human stem cell-derived neurons. We identified both an on-target activation signature, induced by a set of chemically distinct positive allosteric modulators of the N-methyl-d-aspartate (NMDA) receptor, and independent off-target effects. Overall, the protocol and open-source analysis pipeline are a step toward industrializing RNA-seq for high-complexity transcriptomics studies performed at a saturating scale.

BET bromodomain inhibitors regulate keratinocyte plasticity.

Although most acute skin wounds heal rapidly, non-healing skin ulcers represent an increasing and substantial unmet medical need that urgently requires effective therapeutics. Keratinocytes resurface wounds to re-establish the epidermal barrier by transitioning to an activated, migratory state, but this ability is lost in dysfunctional chronic wounds. Small-molecule regulators of keratinocyte plasticity with the potential to reverse keratinocyte malfunction in situ could offer a novel therapeutic approach in skin wound healing. Utilizing high-throughput phenotypic screening of primary keratinocytes, we identify such small molecules, including bromodomain and extra-terminal domain (BET) protein family inhibitors (BETi). BETi induce a sustained activated, migratory state in keratinocytes in vitro, increase activation markers in human epidermis ex vivo and enhance skin wound healing in vivo. Our findings suggest potential clinical utility of BETi in promoting keratinocyte re-epithelialization of skin wounds. Importantly, this novel property of BETi is exclusively observed after transient low-dose exposure, revealing new potential for this compound class.

Gene-signature-derived IC50s/EC50s reflect the potency of causative upstream targets and downstream phenotypes.

Multiplexed gene-signature-based phenotypic assays are increasingly used for the identification and profiling of small molecule-tool compounds and drugs. Here we introduce a method (provided as R-package) for the quantification of the dose-response potency of a gene-signature as EC50 and IC50 values. Two signaling pathways were used as models to validate our methods: beta-adrenergic agonistic activity on cAMP generation (dedicated dataset generated for this study) and EGFR inhibitory effect on cancer cell viability. In both cases, potencies derived from multi-gene expression data were highly correlated with orthogonal potencies derived from cAMP and cell growth readouts, and superior to potencies derived from single individual genes. Based on our results we propose gene-signature potencies as a novel valid alternative for the quantitative prioritization, optimization and development of novel drugs.

DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery.

Here we report Digital RNA with pertUrbation of Genes (DRUG-seq), a high-throughput platform for drug discovery. Pharmaceutical discovery relies on high-throughput screening, yet current platforms have limited readouts. RNA-seq is a powerful tool to investigate drug effects using transcriptome changes as a proxy, yet standard library construction is costly. DRUG-seq captures transcriptional changes detected in standard RNA-seq at 1/100th the cost. In proof-of-concept experiments profiling 433 compounds across 8 doses, transcription profiles generated from DRUG-seq successfully grouped compounds into functional clusters by mechanism of actions (MoAs) based on their intended targets. Perturbation differences reflected in transcriptome changes were detected for compounds engaging the same target, demonstrating the value of using DRUG-seq for understanding on and off-target activities. We demonstrate DRUG-seq captures common mechanisms, as well as differences between compound treatment and CRISPR on the same target. DRUG-seq provides a powerful tool for comprehensive transcriptome readout in a high-throughput screening environment.

Repurposing de novo designed entities reveals phosphodiesterase 3B and cathepsin L modulators.

Using computational bioactivity prediction models we identified phosphodiesterase 3B (PDE3B) and cathepsin L as macromolecular targets of de novo designed compounds. By disclosing the most potent cathepsin L activator known to date, small molecule repurposing by target panel prediction represents a feasible route towards innovative leads for chemical biology and molecular medicine.

Natural-product-derived fragments for fragment-based ligand discovery.

Fragment-based ligand and drug discovery predominantly employs sp(2)-rich compounds covering well-explored regions of chemical space. Despite the ease with which such fragments can be coupled, this focus on flat compounds is widely cited as contributing to the attrition rate of the drug discovery process. In contrast, biologically validated natural products are rich in stereogenic centres and populate areas of chemical space not occupied by average synthetic molecules. Here, we have analysed more than 180,000 natural product structures to arrive at 2,000 clusters of natural-product-derived fragments with high structural diversity, which resemble natural scaffolds and are rich in sp(3)-configured centres. The structures of the cluster centres differ from previously explored fragment libraries, but for nearly half of the clusters representative members are commercially available. We validate their usefulness for the discovery of novel ligand and inhibitor types by means of protein X-ray crystallography and the identification of novel stabilizers of inactive conformations of p38α MAP kinase and of inhibitors of several phosphatases.

Probing the bioactivity-relevant chemical space of robust reactions and common molecular building blocks.

In the search for new bioactive compounds, there is a trend toward increasingly complex compound libraries aiming to target the demanding targets of the future. In contrast, medicinal chemistry and traditional library design rely mainly on a small set of highly established and robust reactions. Here, we probe a set of 58 such reactions for their ability to sample the chemical space of known bioactive molecules, and the potential to create new scaffolds. Combined with ~26,000 common available building blocks, the reactions retrieve around 9% of a scaffold-diverse set of compounds active on human target proteins covering all major pharmaceutical target classes. Almost 80% of generated scaffolds from virtual one-step synthesis products are not present in a large set of known bioactive molecules for human targets, indicating potential for new discoveries. The results suggest that established synthesis resources are well suited to cover the known bioactivity-relevant chemical space and that there are plenty of unexplored regions accessible by these reactions, possibly providing valuable "low-hanging fruit" for hit discovery.

A collection of robust organic synthesis reactions for in silico molecule design.

A focused collection of organic synthesis reactions for computer-based molecule construction is presented. It is inspired by real-world chemistry and has been compiled in close collaboration with medicinal chemists to achieve high practical relevance. Virtual molecules assembled from existing starting material connected by these reactions are supposed to have an enhanced chance to be amenable to real chemical synthesis. About 50% of the reactions in the dataset are ring-forming reactions, which fosters the assembly of novel ring systems and innovative chemotypes. A comparison with a recent survey of the reactions used in early drug discovery revealed considerable overlaps with the collection presented here. The dataset is available encoded as computer-readable Reaction SMARTS expressions from the Supporting Information presented for this paper.

Development of highly potent inhibitors of the Ras-targeting human acyl protein thioesterases based on substrate similarity design.

A matter of common sense: a common recognition motif consisting of a negatively charged group five to six bonds away (red) from the (thio)ester functionality (green) and a positively charged tail group ten to twelve bonds away (blue) was identified in two native acyl protein thioesterase 1 (APT1) substrates. This similarity led to the design of potent inhibitors of the Ras-depalmitoylating enzyme APT1.

Mining for bioactive scaffolds with scaffold networks: improved compound set enrichment from primary screening data.

Identification of meaningful chemical patterns in the increasing amounts of high-throughput-generated bioactivity data available today is an increasingly important challenge for successful drug discovery. Herein, we present the scaffold network as a novel approach for mapping and navigation of chemical and biological space. A scaffold network represents the chemical space of a library of molecules consisting of all molecular scaffolds and smaller "parent" scaffolds generated therefrom by the pruning of rings, effectively leading to a network of common scaffold substructure relationships. This algorithm provides an extension of the scaffold tree algorithm that, instead of a network, generates a tree relationship between a heuristically rule-based selected subset of parent scaffolds. The approach was evaluated for the identification of statistically significantly active scaffolds from primary screening data for which the scaffold tree approach has already been shown to be successful. Because of the exhaustive enumeration of smaller scaffolds and the full enumeration of relationships between them, about twice as many statistically significantly active scaffolds were identified compared to the scaffold-tree-based approach. We suggest visualizing scaffold networks as islands of active scaffolds.

Anticancer properties of an important drug lead podophyllotoxin can be efficiently mimicked by diverse heterocyclic scaffolds accessible via one-step synthesis.

Structural simplification of an antimitotic natural product podophyllotoxin with mimetic heterocyclic scaffolds constructed using multicomponent reactions led to the identification of compounds exhibiting low nanomolar antiproliferative and apoptosis-inducing properties. The most potent compounds were found in the dihydropyridopyrazole, dihydropyridonaphthalene, dihydropyridoindole, and dihydropyridopyrimidine scaffold series. Biochemical mechanistic studies performed with dihydropyridopyrazole compounds showed that these heterocycles inhibit in vitro tubulin polymerization and disrupt the formation of mitotic spindles in dividing cells at low nanomolar concentrations, in a manner similar to podophyllotoxin itself. Separation of a racemic dihydropyridonaphthalene into individual enantiomers demonstrated that only the optical antipode matching the absolute configuration of podophyllotoxin possessed potent anticancer activity. Computer modeling, performed using the podophyllotoxin binding site on ß-tubulin, provided a theoretical understanding of these successful experimental findings.

Recent trends and observations in the design of high-quality screening collections.

The design of a high-quality screening collection is of utmost importance for the early drug-discovery process and provides, in combination with high-quality assay systems, the foundation of future discoveries. Herein, we review recent trends and observations to successfully expand the access to bioactive chemical space, including the feedback from hit assessment interviews of high-throughput screening campaigns; recent successes with chemogenomics target family approaches, the identification of new relevant target/domain families, diversity-oriented synthesis and new emerging compound classes, and non-classical approaches, such as fragment-based screening and DNA-encoded chemical libraries. The role of in silico library design approaches are emphasized.

The scaffold tree: an efficient navigation in the scaffold universe.

The Scaffold Tree algorithm (J Chem Inf Model 47:47-58, 2007) allows to organize large molecular data sets by arranging sets of molecules into a unique tree hierarchy based on their scaffolds, with scaffolds forming leaf nodes of such tree. The hierarchy is created by iterative removal of rings from more complex scaffolds using chemically meaningful set of rules, until a single, root ring is obtained. The classification is deterministic, data set independent, and scales linearly with the number of compounds included in the data set. In this review we summarize the basic principles of the Scaffold Tree methodology and review its applications, which appeared in recent medicinal chemistry literature, including the use of Scaffold Trees for visualization of large chemical data sets, compound clustering, and the identification of novel bioactive molecules. References to several computer programs, including also free tools available on the Internet, allowing to perform classification and visualization of molecules based on their scaffolds are also provided.