Mode Of Action For Compounds Research Articles

Evaluation of a Pooling Chemoproteomics Strategy with an FDA-Approved Drug Library.

Chemoproteomics is a key platform for characterizing the mode of action for compounds, especially for targeted protein degraders such as proteolysis targeting chimeras (PROTACs) and molecular glues. With deep proteome coverage, multiplexed tandem mass tag-mass spectrometry (TMT-MS) can tackle up to 18 samples in a single experiment. Here, we present a pooling strategy for further enhancing the throughput and apply the strategy to an FDA-approved drug library (95 best-in-class compounds). The TMT-MS-based pooling strategy was evaluated in the following steps. First, we demonstrated the capability of TMT-MS by analyzing more than 15 000 unique proteins (> 12 000 gene products) in HEK293 cells treated with five PROTACs (two BRD/BET degraders and three degraders for FAK, ALK, and BTK kinases). We then introduced a rationalized pooling strategy to separate structurally similar compounds in different pools and identified the proteomic response to 14 pools from the drug library. Finally, we validated the proteomic response from one pool by reprofiling the cells via treatment with individual drugs with sufficient replicates. Interestingly, numerous proteins were found to change upon drug treatment, including AMD1, ODC1, PRKX, PRKY, EXO1, AEN, and LRRC58 with 7-hydroxystaurosporine; C6orf64, HMGCR, and RRM2 with Sorafenib; SYS1 and ALAS1 with Venetoclax; and ATF3, CLK1, and CLK4 with Palbocilib. Thus, pooling chemoproteomics screening provides an efficient method for dissecting the molecular targets of compound libraries.

Expanding the search for small-molecule antibacterials by multidimensional profiling.

New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.

Aneugen Versus Clastogen Evaluation and Oxidative Stress-Related Mode-of-Action Assessment of Genotoxic Compounds Using the ToxTracker Reporter Assay.

Understanding the mode-of-action (MOA) of genotoxic compounds and differentiating between direct DNA interaction and indirect genotoxicity is crucial for their reliable safety assessment. ToxTracker is a stem cell-based reporter assay that detects activation of various cellular responses that are associated with genotoxicity and cancer. ToxTracker consists of 6 different GFP reporter cell lines that can detect the induction of DNA damage, oxidative stress, and protein damage in a single test. The assay can thereby provide insight into the MOA of compounds. Genotoxicity is detected in ToxTracker by activation of 2 independent GFP reporters. Activation of the Bscl2-GFP reporter is associated with induction of DNA adducts and subsequent inhibition of DNA replication and the Rtkn-GFP reporter is activated following the formation of DNA double-strand breaks. Here, we show that the differential activation of these 2 genotoxicity reporters could be used to further differentiate between a DNA reactive and clastogenic or a non-DNA-reactive aneugenic MOA of genotoxic compounds. For further classification of aneugenic and clastogenic compounds, the ToxTracker assay was extended with cell cycle analysis and aneuploidy assessment. The extension was validated using a selection of 16 (genotoxic) compounds with a well-established MOA. Furthermore, indirect genotoxicity related to the production of reactive oxygen species was investigated using the DNA damage and oxidative stress ToxTracker reporters in combination with different reactive oxygen species scavengers. With these new extensions, ToxTracker was able to accurately classify compounds as genotoxic or nongenotoxic and could discriminate between DNA-reactive compounds, aneugens, and indirect genotoxicity caused by oxidative stress.

Utilization of CRISPR Interference To Validate MmpL3 as a Drug Target in Mycobacterium tuberculosis

There is an urgent need for novel therapeutics to treat Mycobacterium tuberculosis infections. Genetic strategies for validating novel targets are available, yet their time-consuming nature limits their utility. Here, using MmpL3 as a model target, we report on the application of mycobacterial CRISPR interference for the rapid validation of target essentiality and compound mode of action. This strategy has the potential to rapidly accelerate tuberculosis drug discovery.

An Experimental Pipeline for Initial Characterization of Bacterial Type III Secretion System Inhibitor Mode of Action Using Enteropathogenic Yersinia.

Dozens of Gram negative pathogens use one or more type III secretion systems (T3SS) to disarm host defenses or occupy a beneficial niche during infection of a host organism. While the T3SS represents an attractive drug target and dozens of compounds with T3SS inhibitory activity have been identified, few T3SS inhibitors have been validated and mode of action determined. One issue is the lack of standardized orthogonal assays following high throughput screening. Using a training set of commercially available compounds previously shown to possess T3SS inhibitory activity, we demonstrate the utility of an experiment pipeline comprised of six distinct assays to assess the stages of type III secretion impacted: T3SS gene copy number, T3SS gene expression, T3SS basal body and needle assembly, secretion of cargo through the T3SS, and translocation of T3SS effector proteins into host cells. We used enteropathogenic Yersinia as the workhorse T3SS-expressing model organisms for this experimental pipeline, as Yersinia is sensitive to all T3SS inhibitors we tested, including those active against other T3SS-expressing pathogens. We find that this experimental pipeline is capable of rapidly distinguishing between T3SS inhibitors that interrupt the process of type III secretion at different points in T3SS assembly and function. For example, our data suggests that Compound 3, a malic diamide, blocks either activity of the assembled T3SS or alters the structure of the T3SS in a way that blocks T3SS cargo secretion but not antibody recognition of the T3SS needle. In contrast, our data predicts that Compound 4, a haloid-containing sulfonamidobenzamide, disrupts T3SS needle subunit secretion or assembly. Furthermore, we suggest that misregulation of copy number control of the pYV virulence plasmid, which encodes the Yersinia T3SS, should be considered as a possible mode of action for compounds with T3SS inhibitory activity against Yersinia.

Behavioral screening of the LOPAC1280 library in zebrafish embryos

Spontaneous activity represents an early, primitive form of motor activity within zebrafish embryos, providing a potential readout for identification of neuroactive compounds. However, despite use as an endpoint in chemical screens around the world, the predictive power and limitations of assays relying on spontaneous activity remain unclear. Using an improved high-content screening assay that increased throughput from 384 to 3072 wells per week, we screened a well-characterized library of 1280 pharmacologically active compounds (LOPAC1280) – 612 of which target neurotransmission – to identify which targets are detected using spontaneous activity as a readout. Results from this screen revealed that (1) 8% of the LOPAC1280 library was biologically active; (2) spontaneous activity was affected by compounds spanning a broad array of targets; (3) only 4% of compounds targeting neurotransmission impacted spontaneous activity; and (4) hypoactivity was observed for 100% of hits detected, including those that exhibit opposing mechanisms of action for the same target. Therefore, while this assay was able to rapidly identify potent neuroactive chemicals, these data suggest that spontaneous activity may lack the ability to discriminate modes of action for compounds interfering with neurotransmission, an issue that may be due to systemic uptake following waterborne exposure, persistent control variation, and/or interference with non-neurotransmission-related mechanisms.

Identification of compound modes of action and biomarkers, using DigiWest multiplex protein profiling of materials from multiple species

Identification of compound modes of action and biomarkers, using DigiWest multiplex protein profiling of materials from multiple species

Abstract A206: 2D cell culture can approximate 3D behavior: Applications for screening individual agents and drug combinations.

Abstract Developed and serving over the last 30 years as an indispensible screening tool, 2D in vitro screening has been criticized for poor predictive powers in clinical settings. New tools are under development, e.g. 3D spheroids or “hanging-drops," to overcome these limitations. However, new tools have other restrictions. For example, costs and application for multiple cell lines: model 2D cell lines may not grow as spheroids. We developed a set of analytical and computational models to approximate cell growth upon drug treatment. We could show that for a common 2D cell culture the effect of drug treatment, and therefore interpretation of the results, depends on several factors: seeding cell density, treatment time, cell doubling time, and compound mode of action. To address such limitations we further developed a modified 2D culture. In contrast to a common experimental set-up of low cell density to allow exponential cell growth and short 48-96 hours treatment times, we allowed cells to grow to higher ∼80% confluence densities. Treatment was monitored not as an end-point assay, but in a kinetic mode (every 96 hours) over a period of 4 weeks. We treated 82 cell lines using a set of standard anticancer agents, including 5-FU, paclitaxel, sunitinib, etoposide, lapatinib and eroltinib. Interestingly, some cell lines showing significant responses in classical 2D models, appear to be non-responsive to the drug in dense 2D (D2D) models. This was very obvious for some targeted agents, e.g. EGFRi, probably due to down-regulation of the drug targets in cell lines growing as D2D. In addition, we have applied the same principle to drug combination studies in all 82 cell lines. These results indicate that D2D cell models can approximate cell growth observed in 3D cell cultures by enhancing cell-cell interactions. D2D screening can be performed in hundreds of cell lines (at least 82 different cancer cell lines, as shown here). In contrast to 3D models and end-point detection, D2D also records the drug response kinetics, and can be easily applied for drug combinations. This is especially relevant for slow acting agents, e.g. cytostatic agents. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A206. Citation Format: Igor Ivanov, Danial Taherzadeh, Maximilian Nunziante. 2D cell culture can approximate 3D behavior: Applications for screening individual agents and drug combinations. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A206.

Mechanism of action-based classification of antibiotics using high-content bacterial image analysis.

Image-based screening has become a mature field over the past decade, largely due to the detailed information that can be obtained about compound mode of action by considering the phenotypic effects of test compounds on cellular morphology. However, very few examples exist of extensions of this approach to bacterial targets. We now report the first high-throughput, high-content platform for the prediction of antibiotic modes of action using image-based screening. This approach employs a unique feature segmentation and extraction protocol to quantify key size and shape metrics of bacterial cells over a range of compound concentrations, and matches the trajectories of these metrics to those of training set compounds of known molecular target to predict the test compound's mode of action. This approach has been used to successfully predict the modes of action of a panel of known antibiotics, and has been extended to the evaluation of natural products libraries for the de novo prediction of compound function directly from primary screening data.

Enhancing Cancer Drug Discovery through Novel Cell Signaling Pathway Panel Strategy

The link between signaling pathways and diseases suggests the importance of pathway analysis for drug discovery. This includes target identification and validation, compound mode of action and drug candidate optimization. Here, we propose to apply cell signaling pathway panel approaches for oncology drug discovery. The strategies and guiding principles of the pathway panel approach are discussed. 2 pathway analysis examples with related processes and technology platforms are illustrated to identify cancer drugs that target cancer growth and metastasis. Finally, we highlight potential challenges and opportunities presented by the pathway panel approach.

Small Molecules Targeting Hepatitis C Virus-Encoded NS5A Cause Subcellular Redistribution of Their Target: Insights into Compound Modes of Action

The current standard of care for hepatitis C virus (HCV)-infected patients consists of lengthy treatment with interferon and ribavirin. To increase the effectiveness of HCV therapy, future regimens will incorporate multiple direct-acting antiviral (DAA) drugs. Recently, the HCV-encoded NS5A protein has emerged as a promising DAA target. Compounds targeting NS5A exhibit remarkable potency in vitro and demonstrate early clinical promise, suggesting that NS5A inhibitors could feature in future DAA combination therapies. Since the mechanisms through which these molecules operate are unknown, we have used NS5A inhibitors as tools to investigate their modes of action. Analysis of replicon-containing cells revealed dramatic phenotypic alterations in NS5A localization following treatment with NS5A inhibitors; NS5A was redistributed from the endoplasmic reticulum to lipid droplets. The NS5A relocalization did not occur in cells treated with other classes of HCV inhibitors, and NS5A-targeting molecules did not cause similar alterations in the localization of other HCV-encoded proteins. Time course analysis of the redistribution of NS5A revealed that the transfer of protein to lipid droplets was concomitant with the onset of inhibition, as judged by the kinetic profiles for these compounds. Furthermore, analysis of the kinetic profile of inhibition for a panel of test molecules permitted the separation of compounds into different kinetic classes based on their modes of action. Results from this approach suggested that NS5A inhibitors perturbed the function of new replication complexes, rather than acting on preformed complexes. Taken together, our data reveal novel biological consequences of NS5A inhibition, which may help enable the development of future assay platforms for the identification of new and/or different NS5A inhibitors.

Inference of gene regulatory networks and compound mode of action from time course gene expression profiles

Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. Here we developed an algorithm that can infer the local network of gene-gene interactions surrounding a gene of interest. This is achieved by a perturbation of the gene of interest and subsequently measuring the gene expression profiles at multiple time points. We applied this algorithm to computer simulated data and to experimental data on a nine gene network in Escherichia coli. In this paper we show that it is possible to recover the gene regulatory network from a time series data of gene expression following a perturbation to the cell. We show this both on simulated data and on a nine gene subnetwork part of the DNA-damage response pathway (SOS pathway) in the bacteria E. coli. dibernardo@tigem.it SUPLEMENTARY INFORMATION: Supplementary data are available at http://dibernado.tigem.it

Profiling Cell Networks via Perturbations

The properties of a regulatory network have been determined without the exhaustive examination of each experimental parameter. Gardner et al. explored a network of nine genes in the SOS pathway in Escherichia coli that regulates cellular responses to DNA damage and other stresses. Relatively small changes made near a physiological steady-state point (like that of a cell maintained in culture) allowed these nonlinear cellular processes to be modeled linearly. The transcription activity of each component of the network was altered, and the effects on the abundance of messenger RNA transcripts of the other components were measured. After making some assumptions about the limits of network connectivity and regulatory inputs for each gene, they could identify most of the known regulatory connections in this well-studied pathway. T. S. Gardner, D. di Bernardo, D. Lorenz, J. J. Collins, Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301 , 102-105 (2003). [Abstract] [Full Text]