Functional genomics to uncover drug mechanism of action

Functional CRISPR screens in T cells reveal new opportunities for cancer immunotherapies

T cells are fundamental components in tumour immunity and cancer immunotherapies, which have made immense strides and revolutionized cancer treatment paradigm. However, recent studies delineate the predicament of T cell dysregulation in tumour microenvironment and the compromised efficacy of cancer immunotherapies. CRISPR screens enable unbiased interrogation of gene function in T cells and have revealed functional determinators, genetic regulatory networks, and intercellular interactions in T cell life cycle, thereby providing opportunities to revamp cancer immunotherapies. In this review, we briefly described the central roles of T cells in successful cancer immunotherapies, comprehensively summarised the studies of CRISPR screens in T cells, elaborated resultant master genes that control T cell activation, proliferation, fate determination, effector function, and exhaustion, and highlighted genes (BATF, PRDM1, and TOX) and signalling cascades (JAK-STAT and NF-κB pathways) that extensively engage in multiple branches of T cell responses. In conclusion, this review bridged the gap between discovering element genes to a specific process of T cell activities and apprehending these genes in the global T cell life cycle, deepened the understanding of T cell biology in tumour immunity, and outlined CRISPR screens resources that might facilitate the development and implementation of cancer immunotherapies in the clinic.

Recent advances of targeting nicotinamide phosphoribosyltransferase (NAMPT) for cancer drug discovery

Nicotinamide phosphoribosyltransferase (NAMPT) is the rate-limiting enzyme for the biosynthesis of NAD⁺ in the salvage pathway. NAMPT is overexpressed in various cancers, associating with a poor prognosis and tumor progression. Beyond cancer metabolism, recent evidence unravels additional roles of NAMPT in cancer biology, including DNA repair machinery, crosstalk with oncogenic signaling pathways, cancer cell stemness, and immune responses. NAMPT is a promising therapeutic target for cancer. However, first-generation NAMPT inhibitors exhibited limited efficacy and dose-limiting toxicities in clinical trials. Multiple strategies are being exploited to improve their efficacy and minimize toxic-side effects. This review discusses the biomarkers predictive of response to NAMPT inhibitors, and summarizes the most significant advances in the evolution of structurally distinct NAMPT inhibitors, the manipulation of targeted delivery technologies via antibody-drug conjugates (ADCs), PhotoActivated ChemoTherapy (PACT) and the intratumoral delivery system, as well as the development and pharmacological outcomes of NAMPT degraders. Finally, a discussion of future perspectives and challenges in this area is also included.

Chemical-Genetic Approaches for Exploring Mode of Action of Antifungal Compounds in the Fungal Pathogen Candida albicans

Candida albicans is a prevalent fungal pathogen of humans that can cause both superficial and life-threatening disease, primarily in immunocompromised populations. Currently, antifungal drug classes available to treat fungal infections remain limited and the emergence of drug-resistant strains threatens antifungal efficacy, necessitating the discovery and development of additional therapeutics. The construction of the C. albicans double-barcoded heterozygous deletion collection (DBC) enables the rapid and systematic assessment of haploinsufficiency phenotypes in a pooled format. Specifically, this functional genomics resource can be used to identify heterozygous deletion mutants that are hypersensitive to compounds in order to define putative cellular targets and/or other modifiers of compound activity. Here, we describe protocols to characterize the mode of action of small molecules using the C. albicans DBC, including how to prepare compound-treated cultures, isolate genomic DNA, amplify strain-specific barcodes, and prepare DNA libraries for high-throughput sequencing. This technique provides a powerful approach to elucidate the compound mechanism of action in order to bolster the antifungal pipeline.

Genomic Approaches to Antifungal Drug Target Identification and Validation

The last several decades have witnessed a surge in drug-resistant fungal infections that pose a serious threat to human health. While there is a limited arsenal of drugs that can be used to treat systemic infections, scientific advances have provided renewed optimism for the discovery of novel antifungals. The development of chemical-genomic assays using Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of molecules in a living cell. Advances in molecular biology techniques have enabled complementary assays to be developed in fungal pathogens, including Candida albicans and Cryptococcus neoformans. These approaches enable the identification of target genes for drug candidates, as well as genes involved in buffering drug target pathways. Here, we examine yeast chemical-genomic assays and highlight how such resources can be utilized to predict the mechanisms of action of compounds, to study virulence attributes of diverse fungal pathogens, and to bolster the antifungal pipeline.

Inverse Drug Discovery identifies weak electrophiles affording protein conjugates

Traditional biochemical target-based and phenotypic cell-based screening approaches to drug discovery have produced the current covalent and non-covalent pharmacopoeia. Strategies to expand the druggable proteome include Inverse Drug Discovery, which involves incubating one weak organic electrophile at a time with the proteins of a living cell to identify the conjugates formed. An alkyne substructure in each organic electrophile enables affinity chromatography-mass spectrometry, which produces a list of proteins that each distinct compound reacts with. Herein, we review Inverse Drug Discovery in the context of organic compounds of intermediate complexity harboring Sulfur(VI)-fluoride exchange (SuFEx) electrophiles used to expand the cellular proteins that can be targeted covalently.

Computational analyses of mechanism of action (MoA): data, methods and integration

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.

Target Identification of Small Molecules Using Large-Scale CRISPR-Cas Mutagenesis Scanning of Essential Genes

Target deconvolution of new bioactive agents identified from phenotypic screens remains a challenging task. The discovery of mutations that confer resistance to such agents is regarded as the gold standard proof of target identification. Here, we describe a method that exploits the error-prone repair of CRISPR-induced DNA double-strand breaks to enhance mutagenesis and increase the incidence of drug resistance mutations in essential genes. As each DNA double-strand break is introduced at a targeted genomic site predefined by the presence of a protospacer adjacent motif (PAM) and a particular CRISPR single guide RNA (sgRNA), the genetic location of drug resistance mutations can be easily uncovered through targeted sequencing of CRISPR sgRNAs. Moreover, the method allows for the identification of not only the drug target gene, but also the drug-binding domain within the target gene.

Mammalian Chemical Genomics towards Identifying Targets and Elucidating Modes-of-Action of Bioactive Compounds

The step of identifying target molecules and elucidating the mode of action of bioactive compounds is a major bottleneck for drug discovery from phenotypic screening. Genetic screening for genes that affect drug sensitivity or phenotypes of mammalian cultured cells is a powerful tool to obtain clues to their modes of action. Chemical genomic screening systems for comprehensively identifying such genes or genetic pathways have been established using shRNA libraries for RNA interference-mediated mRNA knockdown or sgRNA libraries for CRISPR/Cas9-mediated gene knockout. The combination of chemical genomic screening in mammalian cells with other approaches such as biochemical searches for target molecules, phenotypic profiling, and yeast genetics provides a systematic way to elucidate the mode of action by converging various pieces of information regarding target molecules, target pathways, and synthetic lethal pathways.

Advances in targeting 'undruggable' transcription factors with small molecules

Transcription factors (TFs) represent key biological players in diseases including cancer, autoimmunity, diabetes and cardiovascular disease. However, outside nuclear receptors, TFs have traditionally been considered 'undruggable' by small-molecule ligands due to significant structural disorder and lack of defined small-molecule binding pockets. Renewed interest in the field has been ignited by significant progress in chemical biology approaches to ligand discovery and optimization, especially the advent of targeted protein degradation approaches, along with increasing appreciation of the critical role a limited number of collaborators play in the regulation of key TF effector genes. Here, we review current understanding of TF-mediated gene regulation, discuss successful targeting strategies and highlight ongoing challenges and emerging approaches to address them.

[From high content screening to target deconvolution: New insights for phenotypic approaches]

The advent of the molecular biology and the completion of the human genome sequencing prompted the pharmaceutical industry to progressively implement target-centric drug discovery strategies. However, concerns regarding the research and development productivity during the last ten years, combined with technological developments in high-content screening, automation, image analysis and artificial intelligence triggered a renewed interest for the phenotypic drug discovery approaches. Target-centric and phenotypic approaches are more and more considered complementary, hence, positioning the target deconvolution on the critical path. This review analyzes the evolution of the target-centric and phenotypic approaches, focusing more specifically on the high-content screening and the target deconvolution technologies currently available.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

A Tale of Two Tails: Efficient Profiling of Protein Degraders by Specific Functional and Target Engagement Readouts

Targeted protein degradation represents an area of great interest, potentially offering improvements with respect to dosing, side effects, drug resistance, and reaching "undruggable" proteins compared with traditional small-molecule therapeutics. A major challenge in the design and characterization of degraders acting as molecular glues is that binding of the molecule to the protein of interest (PoI) is not needed for efficient and selective protein degradation; instead, one needs to understand the interaction with the responsible ligase. Similarly, for proteasome targeting chimeras (PROTACs), understanding the binding characteristics of the PoI alone is not sufficient. Therefore, simultaneously assessing the binding to both PoI and the E3 ligase as well as the resulting degradation profile is of great value. The cellular thermal shift assay (CETSA) is an unbiased cell-based method, designed to investigate the interaction of compounds with their cellular protein targets by measuring compound-induced changes in protein thermal stability. In combination with mass spectrometry (MS), CETSA can simultaneously evaluate compound-induced changes in the stability of thousands of proteins. We have used CETSA MS to profile a number of protein degraders, including molecular glues (e.g., immunomodulatory drugs) and PROTACs, to understand mode of action and to deconvolute off-target effects in intact cells. Within the same experiment, we were able to monitor both target engagement by observing changes in protein thermal stability as well as efficacy by simultaneous assessment of protein abundances. This allowed us to correlate target engagement (i.e., binding to the PoI and ligases) and functional readout (i.e., degrader induced protein degradation).

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no "one size fits all" approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.

Targeting Cdc42 with the anticancer compound MBQ-167 inhibits cell polarity and growth in the budding yeast S. cerevisiae

The Rho GTPase Cdc42 is highly conserved in structure and function. Mechanical or chemical cues in the microenvironment stimulate the localized activation of Cdc42 to rearrange the actin cytoskeleton and establish cell polarity. A role for Cdc42 in cell polarization was first discovered in the budding yeast Saccharomyces cerevisiae, and subsequently shown to also regulate directional motility in animal cells. Accordingly, in cancer Cdc42 promotes migration, invasion, and spread of tumor cells. Therefore, we targeted Cdc42 as a therapeutic strategy to treat metastatic breast cancer and designed the small molecule MBQ-167 as a potent inhibitor against Cdc42 and the homolog Rac. MBQ-167 inhibited cancer cell proliferation and migration in-vitro, and tumor growth and spread in-vivo in a mouse xenograft model of metastatic breast cancer. Since haploid budding yeast express a single Cdc42 gene, and do not express Rac, we used this well characterized model of polarization to define the contribution of Cdc42 inhibition to the effects of MBQ-167 in eukaryotic cells. Growth, budding pattern, and Cdc42 activity was determined in wildtype yeast or cells expressing a conditional knockdown of Cdc42 in response to vehicle or MBQ-167 treatment. As expected, growth and budding polarity were reduced by knocking-down Cdc42, with a parallel effect observed with MBQ-167. Cdc42 activity assays confirmed that MBQ-167 inhibits Cdc42 activation in yeast, and thus, bud polarity. Hence, we have validated MBQ-167 as a Cdc42 inhibitor in another biological context and present a method to screen Cdc42 inhibitors with potential as anti-metastatic cancer drugs.

enAsCas12a Enables CRISPR-Directed Evolution to Screen for Functional Drug Resistance Mutations in Sequences Inaccessible to SpCas9

While drug resistance mutations provide the gold standard proof for drug target engagement, target deconvolution of inhibitors identified from a phenotypic screen remains challenging. Genetic screening for functional in-frame drug resistance mutations by tiling CRISPR-Cas nucleases across protein coding sequences is a method for identifying a drug's target and binding site. However, the applicability of this approach is constrained by the availability of nuclease target sites across genetic regions that mediate drug resistance upon mutation. In this study, we show that an enhanced AsCas12a variant (enAsCas12a), which harbors an expanded targeting range, facilitates screening for drug resistance mutations with increased activity and resolution in regions that are not accessible to other CRISPR nucleases, including the prototypical SpCas9. Utilizing enAsCas12a, we uncover new drug resistance mutations against inhibitors of NAMPT and KIF11. These findings demonstrate that enAsCas12a is a promising new addition to the CRISPR screening toolbox and allows targeting sites not readily accessible to SpCas9.

Advances in fungal chemical genomics for the discovery of new antifungal agents

Invasive fungal infections have escalated from a rare curiosity to a major cause of human mortality around the globe. This is in part due to a scarcity in the number of antifungal drugs available to combat mycotic disease, making the discovery of novel bioactive compounds and determining their mode of action of utmost importance. The development and application of chemical genomic assays using the model yeast Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of diverse molecules in a living cell. Furthermore, complementary assays are continually being developed in fungal pathogens, most notably Candida albicans and Cryptococcus neoformans, to elucidate compound mechanism of action directly in the pathogen of interest. Collectively, the suite of chemical genetic assays that have been developed in multiple fungal species enables the identification of candidate drug target genes, as well as genes involved in buffering drug target pathways, and genes involved in general cellular responses to small molecules. In this review, we examine current yeast chemical genomic assays and highlight how such resources provide powerful tools that can be utilized to bolster the antifungal pipeline.

High-Throughput Screening: today's biochemical and cell-based approaches

High-throughput screening (HTS) provides starting chemical matter in the adventure of developing a new drug. In this review, we survey several HTS methods used today for hit identification, organized in two main flavors: biochemical and cell-based assays. Biochemical assays discussed include fluorescence polarization and anisotropy, FRET, TR-FRET, and fluorescence lifetime analysis. Binding-based methods are also surveyed, including NMR, SPR, mass spectrometry, and DSF. On the other hand, cell-based assays discussed include viability, reporter gene, second messenger, and high-throughput microscopy assays. We devote some emphasis to high-content screening, which is becoming very popular. An advisable stage after hit discovery using phenotypic screens is target deconvolution, and we provide an overview of current chemical proteomics, in silico, and chemical genetics tools. Emphasis is made on recent CRISPR/dCas-based screens. Lastly, we illustrate some of the considerations that inform the choice of HTS methods and point to some areas with potential interest for future research.

Phenotypic Screening of Chemical Libraries Enriched by Molecular Docking to Multiple Targets Selected from Glioblastoma Genomic Data

Like most solid tumors, glioblastoma multiforme (GBM) harbors multiple overexpressed and mutated genes that affect several signaling pathways. Suppressing tumor growth of solid tumors like GBM without toxicity may be achieved by small molecules that selectively modulate a collection of targets across different signaling pathways, also known as selective polypharmacology. Phenotypic screening can be an effective method to uncover such compounds, but the lack of approaches to create focused libraries tailored to tumor targets has limited its impact. Here, we create rational libraries for phenotypic screening by structure-based molecular docking chemical libraries to GBM-specific targets identified using the tumor's RNA sequence and mutation data along with cellular protein-protein interaction data. Screening this enriched library of 47 candidates led to several active compounds, including 1 (IPR-2025), which (i) inhibited cell viability of low-passage patient-derived GBM spheroids with single-digit micromolar IC50 values that are substantially better than standard-of-care temozolomide, (ii) blocked tube-formation of endothelial cells in Matrigel with submicromolar IC50 values, and (iii) had no effect on primary hematopoietic CD34+ progenitor spheroids or astrocyte cell viability. RNA sequencing provided the potential mechanism of action for 1, and mass spectrometry-based thermal proteome profiling confirmed that the compound engages multiple targets. The ability of 1 to inhibit GBM phenotypes without affecting normal cell viability suggests that our screening approach may hold promise for generating lead compounds with selective polypharmacology for the development of treatments of incurable diseases like GBM.

Insights into DNA-drug interactions in the era of omics

Despite the rise of sophisticated new targeting strategies in cancer chemotherapy, many classic DNA-binding drugs remain on the front line of the therapy against cancer. Based on examples primarily from the author's laboratory, this article reviews the capabilities of several DNA-binding drugs to alter gene expression. Research is ongoing about the molecular bases of the inhibition of gene expression and how alteration of the cellular transcriptome can commit cancer cells to die. The development of a variety of omic techniques allows us to gain insights into the effect of antitumor drugs. Genome-wide approaches provide unbiased genomic data that can facilitate a deeper understanding of the cellular response to DNA-binding drugs. Moreover, the results of large-scale genomic studies are gathered in publicly available databases that can be used in developing precision medicine in cancer treatment.

Turning liabilities into opportunities: Off-target based drug repurposing in cancer

Targeted drugs and precision medicine have transformed the landscape of cancer therapy and significantly improved patient outcomes in many cases. However, as therapies are becoming more and more tailored to smaller patient populations and acquired resistance is limiting the duration of clinical responses, there is an ever increasing demand for new drugs, which is not easily met considering steadily rising drug attrition rates and development costs. Considering these challenges drug repurposing is an attractive complementary approach to traditional drug discovery that can satisfy some of these needs. This is facilitated by the fact that most targeted drugs, despite their implicit connotation, are not singularly specific, but rather display a wide spectrum of target selectivity. Importantly, some of the unintended drug "off-targets" are known anticancer targets in their own right. Others are becoming recognized as such in the process of elucidating off-target mechanisms that in fact are responsible for a drug's anticancer activity, thereby revealing potentially new cancer vulnerabilities. Harnessing such beneficial off-target effects can therefore lead to novel and promising precision medicine approaches. Here, we will discuss experimental and computational methods that are employed to specifically develop single target and network-based off-target repurposing strategies, for instance with drug combinations or polypharmacology drugs. By illustrating concrete examples that have led to clinical translation we will furthermore examine the various scientific and non-scientific factors that cumulatively determine the success of these efforts and thus can inform the future development of new and potentially lifesaving off-target based drug repurposing strategies for cancers that constitute important unmet medical needs.

CRISPR/CAS9-based DNA damage response screens reveal gene-drug interactions

DNA damage response (DDR) is critically important for cell survival, genome maintenance, and its defect has been exploited therapeutically in cancer treatment. Many DDR-targeting agents have been generated and have entered the clinic and/or clinical trials. In order to provide a global and unbiased view of DDR network, we designed a focused CRISPR library targeting 365 DDR genes and performed CRISPR screens on the responses to several DDR inhibitors and DNA-damaging agents in 293A cells. With these screens, we determined responsive pathways enriched under treatment with different types of small-molecule agents. Additionally, we showed that POLE3/4-deficient cells displayed enhanced sensitivity to an ATR inhibitor, a PARP inhibitor, and camptothecin. Moreover, by performing DDR screens in isogenic TP53 wild-type and TP53 knock-out cell lines, our results suggest that the performance of our CRISPR DDR dropout screens is independent of TP53 status. Collectively, our findings indicate that CRISPR DDR screens can be used to identify potential targets of small-molecule drugs and reveal that TP53 status does not affect the outcome of these screens.

Systematic Immunotherapy Target Discovery Using Genome-Scale In Vivo CRISPR Screens in CD8 T Cells

CD8 T cells play essential roles in anti-tumor immune responses. Here, we performed genome-scale CRISPR screens in CD8 T cells directly under cancer immunotherapy settings and identified regulators of tumor infiltration and degranulation. The in vivo screen robustly re-identified canonical immunotherapy targets such as PD-1 and Tim-3, along with genes that have not been characterized in T cells. The infiltration and degranulation screens converged on an RNA helicase Dhx37. Dhx37 knockout enhanced the efficacy of antigen-specific CD8 T cells against triple-negative breast cancer in vivo. Immunological characterization in mouse and human CD8 T cells revealed that DHX37 suppresses effector functions, cytokine production, and T cell activation. Transcriptomic profiling and biochemical interrogation revealed a role for DHX37 in modulating NF-κB. These data demonstrate high-throughput in vivo genetic screens for immunotherapy target discovery and establishes DHX37 as a functional regulator of CD8 T cells.

NMR-Based Activity Assays for Determining Compound Inhibition, IC50 Values, Artifactual Activity, and Whole-Cell Activity of Nucleoside Ribohydrolases

NMR spectroscopy is often used for the identification and characterization of enzyme inhibitors in drug discovery, particularly in the context of fragment screening. NMR-based activity assays are ideally suited to work at the higher concentrations of test compounds required to detect these weaker inhibitors. The dynamic range and chemical shift dispersion in an NMR experiment can easily resolve resonances from substrate, product, and test compounds. This contrasts with spectrophotometric assays, in which read-out interference problems often arise from compounds with overlapping UV-vis absorption profiles. In addition, since they lack reporter enzymes, the single-enzyme NMR assays are not prone to coupled-assay false positives. This attribute makes them useful as orthogonal assays, complementing traditional high throughput screening assays and benchtop triage assays. Detailed protocols are provided for initial compound assays at 500 μM and 250 μM, dose-response assays for determining IC50 values, detergent counter screen assays, jump-dilution counter screen assays, and assays in E. coli whole cells. The methods are demonstrated using two nucleoside ribohydrolase enzymes. The use of ¹H NMR is shown for the purine-specific enzyme, while ¹⁹F NMR is shown for the pyrimidine-specific enzyme. The protocols are generally applicable to any enzyme where substrate and product resonances can be observed and distinguished by NMR spectroscopy. To be the most useful in the context of drug discovery, the final concentration of substrate should be no more than 2-3x its Km value. The choice of NMR experiment depends on the enzyme reaction and substrates available as well as available NMR instrumentation.

MoABank: An Integrated Database for Drug Mode of Action Knowledge

Background:

With the declining trend of new drugs yield each year, more comprehensive knowledge of drug MoAs can help identify new applications of available drugs and discovery novel mechanism of drug action.

Objective:

Therefore, construction of a specialized drug mode of action (MoA) database is of paramount importance for new drug research & development.

Methods:

This paper introduces an integrated database for drug mode of action knowledge (MoABank).

Results:

This database can provide the knowledge about drug MoAs, targets, pathways, side effects and other drug-related information for researchers.

Conclusion:

We believe MoABank can make it more convenient for users to obtain the drug MoA information in the future.

Gain- and Loss-of-Function Screens Coupled to Next-Generation Sequencing for Antibiotic Mode of Action and Resistance Studies in Streptococcus pneumoniae

Two whole-genome screening approaches are described for studying the mode of action and the mechanisms of resistance to trimethoprim (TMP) in the Gram-positive Streptococcus pneumoniae The gain-of-function approach (Int-Seq) relies on a genomic library of DNA fragments integrated into a fucose-inducible cassette. The second approach, leading to both gain- and loss-of-function mutation, is based on chemical mutagenesis coupled to next-generation sequencing (Mut-Seq). Both approaches pointed at the drug target dihydrofolate reductase (DHFR) as a major resistance mechanism to TMP. Resistance was achieved by dhfr overexpression either through the addition of fucose (Int-Seq) or by mutations upstream of the gene (Mut-Seq). Three types of mutations increased expression by disrupting a predicted Rho-independent terminator upstream of dhfr Known and novel DHFR mutations were also detected by Mut-Seq, and these were functionally validated for TMP resistance. The two approaches also suggested that an increase in the metabolic flux from purine synthesis to GTP and then to folate can modulate the susceptibility to TMP. Finally, we provide evidence for a novel role of the ABC transporter PatAB in TMP susceptibility. Our genomic screens highlighted novel aspects on the mode of action and mechanisms of resistance to antibiotics.

Identification of Protein Targets of Bioactive Small Molecules Using Randomly Photomodified Probes

Identifying protein targets of bioactive small molecules often requires complex, lengthy development of affinity probes. We present a method for stochastic modification of small molecules of interest with a photoactivatable phenyldiazirine linker. The resulting isomeric mixture is conjugated to a hydrophilic copolymer decorated with biotin and a fluorophore. We validated this approach using known inhibitors of several medicinally relevant enzymes. At least a portion of the stochastic derivatives retained their binding to the target, enabling target visualization, isolation, and identification. Moreover, the mix of stochastic probes could be separated into fractions and tested for binding affinity. The structure of the active probe could be determined and the probe resynthesized to improve binding efficiency. Our approach can thus enable rapid target isolation, identification, and visualization, while providing information required for subsequent synthesis of an optimized probe.

A framework for large-scale metabolome drug profiling links coenzyme A metabolism to the toxicity of anti-cancer drug dichloroacetate

Metabolic profiling of cell line collections has become an invaluable tool to study disease etiology, drug modes of action and to select personalized treatments. However, large-scale in vitro dynamic metabolic profiling is limited by time-consuming sampling and complex measurement procedures. By adapting a mass spectrometry-based metabolomics workflow for high-throughput profiling of diverse adherent mammalian cells, we establish a framework for the rapid measurement and analysis of drug-induced dynamic changes in intracellular metabolites. This methodology is scalable to large compound libraries and is here applied to study the mechanism underlying the toxic effect of dichloroacetate in ovarian cancer cell lines. System-level analysis of the metabolic responses revealed a key and unexpected role of CoA biosynthesis in dichloroacetate toxicity and the more general importance of CoA homeostasis across diverse human cell lines. The herein-proposed strategy for high-content drug metabolic profiling is complementary to other molecular profiling techniques, opening new scientific and drug-discovery opportunities.

Machine learning and image-based profiling in drug discovery

The increase in imaging throughput, new analytical frameworks and high-performance computational resources open new avenues for data-rich phenotypic profiling of small molecules in drug discovery. Image-based profiling assays assessing single-cell phenotypes have been used to explore mechanisms of action, target efficacy and toxicity of small molecules. Technological advances to generate large data sets together with new machine learning approaches for the analysis of high-dimensional profiling data create opportunities to improve many steps in drug discovery. In this review, we will discuss how recent studies applied machine learning approaches in functional profiling workflows with a focus on chemical genetics. While their utility in image-based screening and profiling is predictably evident, examples of novel insights beyond the status quo based on the applications of machine learning approaches are just beginning to emerge. To enable discoveries, future studies also need to develop methodologies that lower the entry barriers to high-throughput profiling experiments by streamlining image-based profiling assays and providing applications for advanced learning technologies such as easy to deploy deep neural networks.

Challenging transcription by DNA-binding antitumor drugs

Cancer has been associated with altered gene expression. Therefore, transcription and its regulation by transcription factors are considered key points to be explored in the pursuit of more efficient antitumor agents. This paper reviews the effects of DNA-binding drugs on the interaction between transcription factors and DNA, and it discusses recent advances in the understanding of the mechanisms by which small compounds interfere with the activity of transcription factors and gene expression. Many DNA-binding drugs, some of them in clinical use, can compete with a variety of transcription factors for their preferred binding sites in gene promoters, or they can covalently modify DNA, thus preventing transcription factors from recognizing their binding sites. On the other hand, transcription factor activity can be impaired through modification of the protein factors or their complexes. Several "omic" tools have been developed to explore the genome-wide changes in gene expression induced by DNA-binding drugs, which reveal details of the mechanisms of action. Transcriptomic profiles obtained from drug-treated cells and of samples collected from patients upon treatment provide insights into the in vivo mechanisms of drug action related to the inhibition of gene transcription. The information available about the molecular structure and mechanisms of action of both transcription factors and DNA-binding drugs, together with the new opportunities provided by functional genomics, should encourage the development of new more-selective DNA-binding antitumor drugs to target a single gene with little effect on others.

Genetic and cellular sensitivity of Caenorhabditis elegans to the chemotherapeutic agent cisplatin

Cisplatin and derivatives are commonly used as chemotherapeutic agents. Although the cytotoxic action of cisplatin on cancer cells is very efficient, clinical oncologists need to deal with two major difficulties, namely the onset of resistance to the drug and the cytotoxic effect in patients. Here, we used Caenorhabditis elegans to investigate factors influencing the response to cisplatin in multicellular organisms. In this hermaphroditic model organism, we observed that sperm failure is a major cause of cisplatin-induced infertility. RNA sequencing data indicate that cisplatin triggers a systemic stress response, in which DAF-16/FOXO and SKN-1/NRF2, two conserved transcription factors, are key regulators. We determined that inhibition of the DNA damage-induced apoptotic pathway does not confer cisplatin protection to the animal. However, mutants for the pro-apoptotic BH3-only gene ced-13 are sensitive to cisplatin, suggesting a protective role of the intrinsic apoptotic pathway. Finally, we demonstrated that our system can also be used to identify mutations providing resistance to cisplatin and therefore potential biomarkers of innate cisplatin-refractory patients. We show that mutants for the redox regulator trxr-1, ortholog of the mammalian thioredoxin reductase 1 TRXR1, display cisplatin resistance. By CRISPR/Cas9, we determined that such resistance relies on the presence of the single selenocysteine residue in TRXR-1.

This article has an associated First Person interview with the first author of the paper.

Summary:Caenorhabditiselegans is a valuable model to identify genetic factors influencing the animal response to the widely used chemotherapeutic agent cisplatin.

Detection of functional protein domains by unbiased genome-wide forward genetic screening

Establishing genetic and chemo-genetic interactions has played key roles in elucidating mechanisms by which certain chemicals perturb cellular functions. In contrast to gene disruption/depletion strategies to identify mechanisms of drug resistance, searching for point-mutational genetic suppressors that can identify separation- or gain-of-function mutations has been limited. Here, by demonstrating its utility in identifying chemical-genetic suppressors of sensitivity to the DNA topoisomerase I poison camptothecin or the poly(ADP-ribose) polymerase inhibitor olaparib, we detail an approach allowing systematic, large-scale detection of spontaneous or chemically-induced suppressor mutations in yeast or haploid mammalian cells in a short timeframe, and with potential applications in other haploid systems. In addition to applications in molecular biology research, this protocol can be used to identify drug targets and predict drug-resistance mechanisms. Mapping suppressor mutations on the primary or tertiary structures of protein suppressor hits provides insights into functionally relevant protein domains. Importantly, we show that olaparib resistance is linked to missense mutations in the DNA binding regions of PARP1, but not in its catalytic domain. This provides experimental support to the concept of PARP1 trapping on DNA as the prime source of toxicity to PARP inhibitors, and points to a novel olaparib resistance mechanism with potential therapeutic implications.

Whole-organism phenotypic screening for anti-infectives promoting host health

To date, antibiotics have been identified on the basis of their ability to kill bacteria or inhibit their growth rather than directly for their capacity to improve clinical outcomes of infected patients. Although historically successful, this approach has led to the development of an antibiotic armamentarium that suffers from a number of shortcomings, including the inevitable emergence of resistance and, in certain infections, suboptimal efficacy leading to long treatment durations, infection recurrence, or high mortality and morbidity rates despite apparent bacterial sterilization. Conventional antibiotics fail to address the complexities of in vivo bacterial physiology and virulence, as well as the role of the host underlying the complex, dynamic interactions that cause disease. New interventions are needed, aimed at host outcome rather than microbiological cure. Here we review the role of screening models for cellular and whole-organism infection, including worms, flies, zebrafish, and mice, to identify novel therapeutic strategies and discuss their future implications.

CRISPR Approaches to Small Molecule Target Identification

A long-standing challenge in drug development is the identification of the mechanisms of action of small molecules with therapeutic potential. A number of methods have been developed to address this challenge, each with inherent strengths and limitations. We here provide a brief review of these methods with a focus on chemical-genetic methods that are based on systematically profiling the effects of genetic perturbations on drug sensitivity. In particular, application of these methods to mammalian systems has been facilitated by the recent advent of CRISPR-based approaches, which enable one to readily repress, induce, or delete a given gene and determine the resulting effects on drug sensitivity.

Target identification of small molecules using large-scale CRISPR-Cas mutagenesis scanning of essential genes

Unraveling the mechanism of action and molecular target of small molecules remains a major challenge in drug discovery. While many cancer drugs target genetic vulnerabilities, loss-of-function screens fail to identify essential genes in drug mechanism of action. Here, we report CRISPRres, a CRISPR-Cas-based genetic screening approach to rapidly derive and identify drug resistance mutations in essential genes. It exploits the local genetic variation created by CRISPR-Cas-induced non-homologous end-joining (NHEJ) repair to generate a wide variety of functional in-frame mutations. Using large sgRNA tiling libraries and known drug-target pairs, we validate it as a target identification approach. We apply CRISPRres to the anticancer agent KPT-9274 and identify nicotinamide phosphoribosyltransferase (NAMPT) as its main target. These results present a powerful and simple genetic approach to create many protein variants that, in combination with positive selection, can be applied to reveal the cellular target of small-molecule inhibitors.

Mining the Potential of Label-Free Biosensors for In Vitro Antipsychotic Drug Screening

The pharmaceutical industry is facing enormous challenges due to high drug attribution rates. For the past decades, novel methods have been developed for safety and efficacy testing, as well as for improving early development stages. In vitro screening methods for drug-receptor binding are considered to be good alternatives for decreasing costs in the identification of drug candidates. However, these methods require lengthy and troublesome labeling steps. Biosensors hold great promise due to the fact that label-free detection schemes can be designed in an easy and low-cost manner. In this paper, for the first time in the literature, we aimed to compare the potential of label-free optical and impedimetric electrochemical biosensors for the screening of antipsychotic drugs (APDs) based on their binding properties to dopamine receptors. Particularly, we have chosen a currently-used atypical antipsychotic drug (Buspirone) for investigating its dopamine D3 receptor (D3R) binding properties using an impedimetric biosensor and a nanoplasmonic biosensor. Both biosensors have been specifically functionalized and characterized for achieving a highly-sensitive and reliable analysis of drug-D3R binding. Our biosensor strategies allow for comparing different affinities against the D3R, which facilitates the identification of strong or weak dopamine antagonists via in vitro assays. This work demonstrates the unique potential of label-free biosensors for the implementation of cost-efficient and simpler analytical tools for the screening of antipsychotic drugs.

Crosstalk between Receptor and Non-receptor Mediated Chemical Modes of Action in Rat Livers Converges through a Dysregulated Gene Expression Network at Tumor Suppressor Tp53

Chemicals, toxicants, and environmental stressors mediate their biologic effect through specific modes of action (MOAs). These encompass key molecular events that lead to changes in the expression of genes within regulatory pathways. Elucidating shared biologic processes and overlapping gene networks will help to better understand the toxicologic effects on biological systems. In this study we used a weighted network analysis of gene expression data from the livers of male Sprague-Dawley rats exposed to chemicals that elicit their effects through receptor-mediated MOAs (aryl hydrocarbon receptor, orphan nuclear hormone receptor, or peroxisome proliferator-activated receptor-α) or non-receptor-mediated MOAs (cytotoxicity or DNA damage). Four gene networks were highly preserved and statistically significant in each of the two MOA classes. Three of the four networks contain genes that enrich for immunity and defense. However, many canonical pathways related to an immune response were activated from exposure to the non-receptor-mediated MOA chemicals and deactivated from exposure to the receptor-mediated MOA chemicals. The top gene network contains a module with 33 genes including tumor suppressor TP53 as the central hub which was slightly up-regulated in gene expression compared to control. Although, there is crosstalk between the two MOA classes of chemicals at the TP53 gene network, more than half of the genes are dysregulated in opposite directions. For example, Thromboxane A Synthase 1 (Tbxas1), a cytochrome P450 protein coding gene regulated by Tp53, is down-regulated by exposure to the receptor-mediated chemicals but up-regulated by the non-receptor-mediated chemicals. The regulation of gene expression by the chemicals within MOA classes was consistent despite varying alanine transaminase and aspartate aminotransferase liver enzyme measurements. These results suggest that overlap in toxicologic pathways by chemicals with different MOAs provides common mechanisms for discordant regulation of gene expression within molecular networks.

Combined CRISPRi/a-Based Chemical Genetic Screens Reveal that Rigosertib Is a Microtubule-Destabilizing Agent

Chemical libraries paired with phenotypic screens can now readily identify compounds with therapeutic potential. A central limitation to exploiting these compounds, however, has been in identifying their relevant cellular targets. Here, we present a two-tiered CRISPR-mediated chemical-genetic strategy for target identification: combined genome-wide knockdown and overexpression screening as well as focused, comparative chemical-genetic profiling. Application of these strategies to rigosertib, a drug in phase 3 clinical trials for high-risk myelodysplastic syndrome whose molecular target had remained controversial, pointed singularly to microtubules as rigosertib's target. We showed that rigosertib indeed directly binds to and destabilizes microtubules using cell biological, in vitro, and structural approaches. Finally, expression of tubulin with a structure-guided mutation in the rigosertib-binding pocket conferred resistance to rigosertib, establishing that rigosertib kills cancer cells by destabilizing microtubules. These results demonstrate the power of our chemical-genetic screening strategies for pinpointing the physiologically relevant targets of chemical agents.

Feedback activation of AMPK-mediated autophagy acceleration is a key resistance mechanism against SCD1 inhibitor-induced cell growth inhibition

Elucidating the bioactive compound modes of action is crucial for increasing success rates in drug development. For anticancer drugs, defining effective drug combinations that overcome resistance improves therapeutic efficacy. Herein, by using a biologically annotated compound library, we performed a large-scale combination screening with Stearoyl-CoA desaturase-1 (SCD1) inhibitor, T-3764518, which partially inhibits colorectal cancer cell proliferation. T-3764518 induced phosphorylation and activation of AMPK in HCT-116 cells, which led to blockade of downstream fatty acid synthesis and acceleration of autophagy. Attenuation of fatty acid synthesis by small molecules suppressed the growth inhibitory effect of T-3764518. In contrast, combination of T-3764518 with autophagy flux inhibitors synergistically inhibited cellular proliferation. Experiments using SCD1 knock-out cells validated the results obtained with T-3764518. The results of our study indicated that activation of autophagy serves as a survival signal when SCD1 is inhibited in HCT-116 cells. Furthermore, these findings suggest that combining SCD1 inhibitor with autophagy inhibitors is a promising anticancer therapy.

Opportunities and challenges in phenotypic drug discovery: an industry perspective

Phenotypic drug discovery (PDD) approaches do not rely on knowledge of the identity of a specific drug target or a hypothesis about its role in disease, in contrast to the target-based strategies that have been widely used in the pharmaceutical industry in the past three decades. However, in recent years, there has been a resurgence in interest in PDD approaches based on their potential to address the incompletely understood complexity of diseases and their promise of delivering first-in-class drugs, as well as major advances in the tools for cell-based phenotypic screening. Nevertheless, PDD approaches also have considerable challenges, such as hit validation and target deconvolution. This article focuses on the lessons learned by researchers engaged in PDD in the pharmaceutical industry and considers the impact of 'omics' knowledge in defining a cellular disease phenotype in the era of precision medicine, introducing the concept of a chain of translatability. We particularly aim to identify features and areas in which PDD can best deliver value to drug discovery portfolios and can contribute to the identification and the development of novel medicines, and to illustrate the challenges and uncertainties that are associated with PDD in order to help set realistic expectations with regard to its benefits and costs.

High-content drug screening for rare diseases

Per definition, rare diseases affect only a small number of subjects within a given population. Taken together however, they represent a considerable medical burden, which remains poorly addressed in terms of treatment. Compared to other diseases, obstacles to the development of therapies for rare diseases include less extensive physiopathology knowledge, limited number of patients to test treatments, and poor commercial interest from the industry. Recently, advances in high-throughput and high-content screening (HTS and HCS) have been fostered by the development of specific routines that use robot- and computer-assisted technologies to automatize tasks, allowing screening of a large number of compounds in a short period of time, using experimental model of diseases. These approaches are particularly relevant for drug repositioning in rare disease, which restricts the search to compounds that have already been tested in humans, thereby reducing the need for extensive preclinical tests. In the future, these same tools, combined with computational modeling and artificial neural network analyses, may also be used to predict individual clinical responses to drugs in a personalized medicine approach.

Network-assisted target identification for haploinsufficiency and homozygous profiling screens

Chemical genomic screens have recently emerged as a systematic approach to drug discovery on a genome-wide scale. Drug target identification and elucidation of the mechanism of action (MoA) of hits from these noisy high-throughput screens remain difficult. Here, we present GIT (Genetic Interaction Network-Assisted Target Identification), a network analysis method for drug target identification in haploinsufficiency profiling (HIP) and homozygous profiling (HOP) screens. With the drug-induced phenotypic fitness defect of the deletion of a gene, GIT also incorporates the fitness defects of the gene's neighbors in the genetic interaction network. On three genome-scale yeast chemical genomic screens, GIT substantially outperforms previous scoring methods on target identification on HIP and HOP assays, respectively. Finally, we showed that by combining HIP and HOP assays, GIT further boosts target identification and reveals potential drug's mechanism of action.

A quantitative shRNA screen identifies ATP1A1 as a gene that regulates cytotoxicity by aurilide B

Genome-wide RNA interference (RNAi) with pooled and barcoded short-hairpin RNA (shRNA) libraries provides a powerful tool for identifying cellular components that are relevant to the modes/mechanisms of action (MoA) of bioactive compounds. shRNAs that affect cellular sensitivity to a given compound can be identified by deep sequencing of shRNA-specific barcodes. We used multiplex barcode sequencing technology by adding sample-specific index tags to PCR primers during sequence library preparation, enabling parallel analysis of multiple samples. An shRNA library screen with this system revealed that downregulation of ATP1A1, an α-subunit of Na⁺/K⁺ ATPase, conferred significant sensitivity to aurilide B, a natural marine product that induces mitochondria-mediated apoptosis. Combined treatment with ouabain which inhibits Na⁺/K⁺ ATPase by targeting α-subunits potentiated sensitivity to aurilide B, suggesting that ATP1A1 regulates mitochondria-mediated apoptosis. Our results indicate that multiplex sequencing facilitates the use of pooled shRNA library screening for the identification of combination drug therapy targets.

Applications of chemogenomic library screening in drug discovery

The allure of phenotypic screening, combined with the industry preference for target-based approaches, has prompted the development of innovative chemical biology technologies that facilitate the identification of new therapeutic targets for accelerated drug discovery. A chemogenomic library is a collection of selective small-molecule pharmacological agents, and a hit from such a set in a phenotypic screen suggests that the annotated target or targets of that pharmacological agent may be involved in perturbing the observable phenotype. In this Review, we describe opportunities for chemogenomic screening to considerably expedite the conversion of phenotypic screening projects into target-based drug discovery approaches. Other applications are explored, including drug repositioning, predictive toxicology and the discovery of novel pharmacological modalities.