Target deconvolution techniques in modern phenotypic profiling

A novel approach for target deconvolution from phenotype-based screening using knowledge graph

Deconvoluting drug targets is crucial in modern drug development, yet both traditional and artificial intelligence (AI)-driven methods face challenges in terms of completeness, accuracy, and efficiency. Identifying drug targets, especially within complex systems such as the p53 pathway, remains a formidable task. The regulation of this pathway by myriad stress signals and regulatory elements adds layers of complexity to the discovery of effective p53 pathway activators. Recent insights into p53 activation have led to two main screening strategies for p53 activators. The target-based approach focuses on p53 and its regulators (MDM2, MDMX, USP7, Sirt proteins), but requires separate systems for each target and may miss multi-target compounds. Phenotype-based screening can reveal new targets but involves a lengthy process to elucidate mechanisms and targets, hindering drug development. Knowledge graphs have emerged as powerful tools that offer strengths in link prediction and knowledge inference to address these issues. In this study, we constructed a protein-protein interaction knowledge graph (PPIKG) and pioneered an integrated drug target deconvolution system that combines AI with molecular docking techniques. Analysis based on the PPIKG narrowed down candidate proteins from 1088 to 35, significantly saving time and cost. Subsequent molecular docking led us to pinpoint USP7 as a direct target for the p53 pathway activator UNBS5162. Leveraging knowledge graphs and a multidisciplinary approach allows us to streamline the laborious and expensive process of reverse targeting drug discovery through phenotype screening. Our findings have the potential to revolutionize drug screening and open new avenues in pharmacological research, increasing the speed and efficiency of pursuing novel therapeutics. The code is available at  https://github.com/Xiong-Jing/PPIKG .

Bridging the gap between target-based and phenotypic-based drug discovery

INTRODUCTION

The unparalleled progress in science of the last decades has brought a better understanding of the molecular mechanisms of diseases. This promoted drug discovery processes based on a target approach. However, despite the high promises associated, a critical decrease in the number of first-in-class drugs has been observed.

AREAS COVERED

This review analyses the challenges, advances, and opportunities associated with the main strategies of the drug discovery process, i.e. based on a rational target approach and on an empirical phenotypic approach. This review also evaluates how the gap between these two crossroads can be bridged toward a more efficient drug discovery process.

EXPERT OPINION

The critical lack of knowledge of the complex biological networks is leading to targets not relevant for the clinical context or to drugs that present undesired adverse effects. The phenotypic systems designed by considering available molecular mechanisms can mitigate these knowledge gaps. Associated with the expansion of the chemical space and other technologies, these designs can lead to more efficient drug discoveries. Technological and scientific knowledge should also be applied to identify, as early as possible, both drug targets and mechanisms of action, leading to a more efficient drug discovery pipeline.

Integrated Proteomics Characterization of NLRP3 Inflammasome Inhibitor MCC950 in Monocytic Cell Line Confirms Direct MCC950 Engagement with Endogenous NLRP3

Inhibition of the NLRP3 inflammasome is a promising strategy for the development of new treatments for inflammatory diseases. MCC950 is a potent and selective small-molecule inhibitor of the NLRP3 pathway and has been validated in numerous species and disease models. Although the capacity of MCC950 to block NLRP3 signaling is well-established, it is still critical to identify the mechanism of action and molecular targets of MCC950 to inform and derisk drug development. Quantitative proteomics performed in disease-relevant systems provides a powerful method to study both direct and indirect pharmacological responses to small molecules to elucidate the mechanism of action and confirm target engagement. A comprehensive target deconvolution campaign requires the use of complementary chemical biology techniques. Here we applied two orthogonal chemical biology techniques: compressed Cellular Thermal Shift Assay (CETSA) and photoaffinity labeling chemoproteomics, performed under biologically relevant conditions with LPS-primed THP-1 cells, thereby deconvoluting, for the first time, the molecular targets of MCC950 using chemical biology techniques. In-cell chemoproteomics with inlysate CETSA confirmed the suspected mechanism as the disruption of inflammasome formation via NLRP3. Further cCETSA (c indicates compressed) in live cells mapped the stabilization of NLRP3 inflammasome pathway proteins, highlighting modulation of the targeted pathway. This is the first evidence of direct MCC950 engagement with endogenous NLRP3 in a human macrophage cellular system using discovery proteomics chemical biology techniques, providing critical information for inflammasome studies.

Ligand Modification-Free Methods for the Profiling of Protein-Environmental Chemical Interactions

Adverse health outcomes caused by environmental chemicals are often initiated via their interactions with proteins. Essentially, one environmental chemical may interact with a number of proteins and/or a protein may interact with a multitude of environmental chemicals, forming an intricate interaction network. Omics-wide protein-environmental chemical interaction profiling (PECI) is of prominent importance for comprehensive understanding of these interaction networks, including the toxicity mechanisms of action (MoA), and for providing systematic chemical safety assessment. However, such information remains unknown for most environmental chemicals, partly due to their vast chemical diversity. In recent years, with the continuous efforts afforded, especially in mass spectrometry (MS) based omics technologies, several ligand modification-free methods have been developed, and new attention for systematic PECI profiling was gained. In this Review, we provide a comprehensive overview on these methodologies for the identification of ligand-protein interactions, including affinity interaction-based methods of affinity-driven purification, covalent modification profiling, and activity-based protein profiling (ABPP) in a competitive mode, physicochemical property changes assessment methods of ligand-directed nuclear magnetic resonance (ligand-directed NMR), MS integrated with equilibrium dialysis for the discovery of allostery systematically (MIDAS), thermal proteome profiling (TPP), limited proteolysis-coupled mass spectrometry (LiP-MS), stability of proteins from rates of oxidation (SPROX), and several intracellular downstream response characterization methods. We expect that the applications of these ligand modification-free technologies will drive a considerable increase in the number of PECI identified, facilitate unveiling the toxicological mechanisms, and ultimately contribute to systematic health risk assessment of environmental chemicals.

Immunogenic cell death (ICD) enhancers-Drugs that enhance the perception of ICD by dendritic cells

The search for immunostimulatory drugs applicable to cancer immunotherapy may profit from target-agnostic methods in which agents are screened for their functional impact on immune cells cultured in vitro without any preconceived idea on their mode of action. We have built a synthetic mini-immune system in which stressed and dying cancer cells (derived from standardized cell lines) are confronted with dendritic cells (DCs, derived from immortalized precursors) and CD8+ T-cell hybridoma cells expressing a defined T-cell receptor. Using this system, we can identify three types of immunostimulatory drugs: (i) pharmacological agents that stimulate immunogenic cell death (ICD) of malignant cells; (ii) drugs that act on DCs to enhance their response to ICD; and (iii) drugs that act on T cells to increase their effector function. Here, we focus on strategies to develop drugs that enhance the perception of ICD by DCs and to which we refer as "ICD enhancers." We discuss examples of ICD enhancers, including ligands of pattern recognition receptors (exemplified by TLR3 ligands that correct the deficient function of DCs lacking FPR1) and immunometabolic modifiers (exemplified by hexokinase-2 inhibitors), as well as methods for target deconvolution applicable to the mechanistic characterization of ICD enhancers.

New Therapeutic Perspectives in Prostate Cancer: Patient-Derived Organoids and Patient-Derived Xenograft Models in Precision Medicine

One of the major goals in the advancement of basic cancer research focuses on the development of new anticancer therapies. To understand the molecular mechanisms of cancer progression, acquired drug resistance, and the metastatic process, the use of preclinical in vitro models that faithfully summarize the properties of the tumor in patients is still a necessity. The tumor is represented by a diverse group of cell clones, and in recent years, to reproduce in vitro preclinical tumor models, monolayer cell cultures have been supplanted by patient-derived xenograft (PDX) models and cultured organoids derived from the patient (PDO). These models have proved indispensable for the study of the tumor microenvironment (TME) and its interaction with tumor cells. Prostate cancer (PCa) is the most common neoplasia in men in the world. It is characterized by genomic instability and resistance to conventional therapies. Despite recent advances in diagnosis and treatment, PCa remains a leading cause of cancer death. Here, we review the studies of the last 10 years as the number of papers is growing very fast in the field. We also discuss the discovered limitations and the new challenges in using the organoid culture system and in using PDXs in studying the prostate cancer phenotype, performing drug testing, and developing anticancer molecular therapies.

Cellular Exposure to Chloroacetanilide Herbicides Induces Distinct Protein Destabilization Profiles

Herbicides in the widely used chloroacetanilide class harbor a potent electrophilic moiety, which can damage proteins through nucleophilic substitution. In general, damaged proteins are subject to misfolding. Accumulation of misfolded proteins compromises cellular integrity by disrupting cellular proteostasis networks, which can further destabilize the cellular proteome. While direct conjugation targets can be discovered through affinity-based protein profiling, there are few approaches to probe how cellular exposure to toxicants impacts the stability of the proteome. We apply a quantitative proteomics methodology to identify chloroacetanilide-destabilized proteins in HEK293T cells based on their binding to the H31Q mutant of the human Hsp40 chaperone DNAJB8. We find that a brief cellular exposure to the chloroacetanilides acetochlor, alachlor, and propachlor induces misfolding of dozens of cellular proteins. These herbicides feature distinct but overlapping profiles of protein destabilization, highly concentrated in proteins with reactive cysteine residues. Consistent with the recent literature from the pharmacology field, reactivity is driven by neither inherent nucleophilic nor electrophilic reactivity but is idiosyncratic. We discover that propachlor induces a general increase in protein aggregation and selectively targets GAPDH and PARK7, leading to a decrease in their cellular activities. Hsp40 affinity profiling identifies a majority of propachlor targets identified by competitive activity-based protein profiling (ABPP), but ABPP can only identify about 10% of protein targets identified by Hsp40 affinity profiling. GAPDH is primarily modified by the direct conjugation of propachlor at a catalytic cysteine residue, leading to global destabilization of the protein. The Hsp40 affinity strategy is an effective technique to profile cellular proteins that are destabilized by cellular toxin exposure. Raw proteomics data is available through the PRIDE Archive at PXD030635.

In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.

Platform combining statistical modeling and patient-derived organoids to facilitate personalized treatment of colorectal carcinoma

BACKGROUND

We propose a new approach for designing personalized treatment for colorectal cancer (CRC) patients, by combining ex vivo organoid efficacy testing with mathematical modeling of the results.

METHODS

The validated phenotypic approach called Therapeutically Guided Multidrug Optimization (TGMO) was used to identify four low-dose synergistic optimized drug combinations (ODC) in 3D human CRC models of cells that are either sensitive or resistant to first-line CRC chemotherapy (FOLFOXIRI). Our findings were obtained using second order linear regression and adaptive lasso.

RESULTS

The activity of all ODCs was validated on patient-derived organoids (PDO) from cases with either primary or metastatic CRC. The CRC material was molecularly characterized using whole-exome sequencing and RNAseq. In PDO from patients with liver metastases (stage IV) identified as CMS4/CRIS-A, our ODCs consisting of regorafenib [1 mM], vemurafenib [11 mM], palbociclib [1 mM] and lapatinib [0.5 mM] inhibited cell viability up to 88%, which significantly outperforms FOLFOXIRI administered at clinical doses. Furthermore, we identified patient-specific TGMO-based ODCs that outperform the efficacy of the current chemotherapy standard of care, FOLFOXIRI.

CONCLUSIONS

Our approach allows the optimization of patient-tailored synergistic multi-drug combinations within a clinically relevant timeframe.

Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.

Proteome-Wide Fragment-Based Ligand and Target Discovery

Chemical probes are invaluable tools to investigate biological processes and can serve as lead molecules for the development of new therapies. However, despite their utility, only a fraction of human proteins have selective chemical probes, and more generally, our knowledge of the "chemically-tractable" proteome is limited, leaving many potential therapeutic targets unexploited. To help address these challenges, powerful chemical proteomic approaches have recently been developed to globally survey the ability of proteins to bind small molecules (i. e., ligandability) directly in native systems. In this review, we discuss the utility of such approaches, with a focus on the integration of chemoproteomic methods with fragment-based ligand discovery (FBLD), to facilitate the broad mapping of the ligandable proteome while also providing starting points for progression into lead chemical probes.

Phenotype-based drug screening: An in vivo strategy to classify and identify the chemical compounds modulating zebrafish M-cell regeneration

Several disease-modulatory FDA-approved drugs are being used in patients with neurodegenerative diseases. However, information on their toxicity-related profiles is very limited. Therefore, measurement of drug toxicity is essential to increase the knowledge of their side effects. This study aimed to identify compounds that can modulate M-cell regeneration by causing neuro-protection and -toxicity. Here, we developed a simple and efficient in vivo assay using Tg (hsp: Gal4FF62A; UAS: nfsB-mCherry) transgenic zebrafish larvae. Interestingly, via the phenotype-based drug screening approach, we rapidly investigated 1,260 compounds from the United States drug collection and validated these in large numbers, including 14 compounds, that were obstructing this regeneration process. Next, 4 FDA-approved drugs out of 14 compounds were selected as the lead hits for in silico analysis to clarify their binding patterns with PTEN and SOCS3 signaling due to their significant potential in the inhibition of axon regeneration. Molecular docking studies indicated good binding affinity of all 4 drugs with the respective signaling molecules. This may point to PTEN and SOCS3 as the signaling molecules responsible for reducing axon regeneration. Moreover, the acute effect of compounds in reducing M-cell regeneration delineated their toxic effect. In conclusion, our in vivo along with in silico screening strategy will promote the rapid translation of new therapeutics to improve knowledge of the toxicity profile of approved/non-approved drugs efficiently.

A clickable photoaffinity probe of betulinic acid identifies tropomyosin as a target

Target identification of bioactive compounds is important for understanding their mechanisms of action and provides critical insights into their therapeutic utility. While it remains a challenge, unbiased chemoproteomics strategy using clickable photoaffinity probes is a useful and validated approach for target identification. One major limitation of this approach is the efficient synthesis of appropriately substituted clickable photoaffinity probes. Herein, we describe an efficient and consistent method to prepare such probes. We further employed this method to prepare a highly stereo-congested probe based on naturally occurring triterpenoid betulinic acid. With this photoaffinity probe, we identified tropomyosin as a novel target for betulinic acid that can account for the unique biological phenotype on cellular cytoskeleton induced by betulinic acid.

Challenging Reverse Screening: A Benchmark Study for Comprehensive Evaluation

As an efficient way of computational target prediction, reverse docking can find not only potential targets but also binding modes for a query ligand. Though the number of available docking tools keeps expanding, there is still not a comprehensive evaluation study which can uncover the advantages and limitations of these strategies in the research field of computational target-fishing. In this study, we propose a brand-new evaluation dataset tailor-made for reverse docking, which is composed of a true positive set (the core set) and two negative sets (the similar decoy set and the dissimilar decoy set). The proposed evaluation dataset can assess the prediction performance of docking tools as various values affected by varying degrees of inter-target ranking bias. The performance of four classical docking programs (AutoDock, AutoDock Vina, Glide and GOLD) was evaluated utilizing our dataset, and a biased prediction performance was observed regarding binding site properties. The results demonstrated that Glide (SP) and Glide(XP) had the best capacity to find true targets whether there was inter-target ranking bias or not.

The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Phenotypic Drug Discovery

Phenotypic drug discovery has a long track record of delivering innovative drugs and has received renewed attention in the last few years. The promise of this approach, however, comes with several challenges that should be addressed to avoid wasting time and resources on drugs with undesired modes of action or, worse, false-positive hits. In this set of best practices, we go over the essential steps of phenotypic drug discovery and provide guidance on how to increase the chance of success in identifying validated and relevant chemical starting points for optimization: selecting the right assay, selecting the right compound screening library and developing appropriate hit validation assays. Then, we highlight the importance of initiating studies to determine the mode of action of the identified hits early and present the current state of the art.

The right tools for the job: the central role for next generation chemical probes and chemistry-based target deconvolution methods in phenotypic drug discovery

The reconnection of the scientific community with phenotypic drug discovery has created exciting new possibilities to develop therapies for diseases with highly complex biology. It promises to revolutionise fields such as neurodegenerative disease and regenerative medicine, where the development of new drugs has consistently proved elusive. Arguably, the greatest challenge in readopting the phenotypic drug discovery approach exists in establishing a crucial chain of translatability between phenotype and benefit to patients in the clinic. This remains a key stumbling block for the field which needs to be overcome in order to fully realise the potential of phenotypic drug discovery. Excellent quality chemical probes and chemistry-based target deconvolution techniques will be a crucial part of this process. In this review, we discuss the current capabilities of chemical probes and chemistry-based target deconvolution methods and evaluate the next advances necessary in order to fully support phenotypic screening approaches in drug discovery.

A comprehensive screening method for investigating the potential binding targets of doxorubicin based on protein microarray

With the development of precision therapy, pharmacological research pays more and more attention to seek and confirm the target of drugs in order to understand the mechanism of drug action and reduce side effects. Screening candidate proteins can be effectively used to predict potential drug targets and toxicity. Therefore, a high-throughput drug-binding protein screening method based on protein microarray which contains over 21,000 human proteins was introduced in this investigation. Doxorubicin, a classical chemotherapeutic agent widely used in clinical treatment, was taken as a drug example in our protein screening study. Through microarray and bioinformatics analysis, more potential targets were found with different binding affinity to doxorubicin, and HRAS stands out as a critical protein from candidate proteins. In addition, the results revealed that the formation of the HRAS-RAF complex is promoted by doxorubicin. It is our expectation that the outcomes could benefit to understand the various effect of the doxorubicin and push the protein microarray screening to apply in the comprehensive pharmacological and toxicological investigation of other drugs.

Label-Free Target Identification and Confirmation Using Thermal Stability Shift Assays

Target identification presents one of the biggest challenges to chemical genomic approaches. In recent years, several methods have been applied for target identification and validation in plant cells. Here, we describe a label-free method based on the thermodynamic stabilization of a protein by interaction with a small-molecule ligand. With increasing temperature, proteins undergo thermal denaturation resulting in irreversible aggregation and precipitation. The binding of a small molecule to its target can enhance protein stability resulting in an increased temperature of aggregation (T_agg). This distinct increase in the temperature of aggregation known as a thermal shift can identify a compound-target protein interaction in high-throughput assays or, validate a predicted interaction.

Single-cell infrared phenomics: phenotypic screening with infrared microspectroscopy

We conceptually demonstrate single-cell infrared phenomics as a novel strategy of phenotypic screening with infrared microspectroscopy. Based on this development, the cancer cell HepG2 glycocalyx was first identified as a potential target of protopanaxadiol, an herbal medicine. These findings provide a powerful tool to accurately evaluate the cell stress response and to largely expand the phenotypic screening toolkit for drug discovery.

High-throughput synthetic rescue for exhaustive characterization of suppressor mutations in human genes

Inherited or acquired mutations can lead to pathological outcomes. However, in a process defined as synthetic rescue, phenotypic outcome created by primary mutation is alleviated by suppressor mutations. An exhaustive characterization of these mutations in humans is extremely valuable to better comprehend why patients carrying the same detrimental mutation exhibit different pathological outcomes or different responses to treatment. Here, we first review all known suppressor mutations' mechanisms characterized by genetic screens on model species like yeast or flies. However, human suppressor mutations are scarce, despite some being discovered based on orthologue genes. Because of recent advances in high-throughput screening, developing an inventory of human suppressor mutations for pathological processes seems achievable. In addition, we review several screening methods for suppressor mutations in cultured human cells through knock-out, knock-down or random mutagenesis screens on large scale. We provide examples of studies published over the past years that opened new therapeutic avenues, particularly in oncology.

A combinatorial target screening strategy for deorphaning macromolecular targets of natural product

Natural products, as an ideal starting point for molecular design, play a pivotal role in drug discovery; however, ambiguous targets and mechanisms have limited their in-depth research and applications in a global dimension. In-silico target prediction methods have become an alternative to target identification experiments due to the high accuracy and speed, but most studies only use a single prediction method, which may reduce the accuracy and reliability of the prediction. Here, we firstly presented a combinatorial target screening strategy to facilitate multi-target screening of natural products considering the characteristics of diverse in-silico target prediction methods, which consists of ligand-based online approaches, consensus SAR modelling and target-specific re-scoring function modelling. To validate the practicability of the strategy, natural product neferine, a bisbenzylisoquinoline alkaloid isolated from the lotus seed, was taken as an example to illustrate the screening process and a series of corresponding experiments were implemented to explore the pharmacological mechanisms of neferine. The proposed computational method could be used for a complementary hypothesis generation and rapid analysis of potential targets of natural products.

Microparticle-Assisted Precipitation Screening Method for Robust Drug Target Identification

While thermal proteome profiling (TPP) shines in the field of drug target screening by analyzing the soluble fraction of the proteome samples treated at high temperature, the counterpart, the insoluble precipitate, has been overlooked for a long time. The analysis of the precipitate is hampered by the inefficient sample processing procedure. Herein, we propose a novel method, termed microparticle-assisted precipitation screening (MAPS), for drug target identification. The MAPS method exploits the principle that drug-bound proteins will be more resistant to thermal unfolding similar to the classic TPP method, but the process of protein precipitation is assisted by microparticles. Upon heating, proteins unfold and aggregate on the surface of the microparticles. The introduction of a microparticle simplifies the whole sample preparation workflow. The proteins that precipitate on the microparticles are subjected to washing, alkylation, and digestion. The whole sample preparation is processed conveniently on the surface of the microparticles without any transfer. With the assistance of microparticles, sample loss is minimized. The MAPS method is compatible with minute amounts of initial proteins. MAPS was applied to screen the targets of several well-studied drugs and the known target proteins were successfully identified with high confidence and specificity. To investigate the specificity of the method, MAPS was applied to screen the targets of the pan-kinase inhibitor, staurosporine, and 32 protein kinases (specificity of 80%) were identified using only 20 μg of initial proteins of each sample. MAPS is an unbiased robust method for drug target screening, filling the vacancy of stability-based target screening using a precipitate.

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.

Secretome-Based Screening in Target Discovery

Secreted proteins and their cognate plasma membrane receptors regulate human physiology by transducing signals from the extracellular environment into cells resulting in different cellular phenotypes. Systematic use of secretome proteins in assays enables discovery of novel biology and signaling pathways. Several secretome-based phenotypic screening platforms have been described in the literature and shown to facilitate target identification in drug discovery. In this review, we summarize the current status of secretome-based screening. This includes annotation, production, quality control, and sample management of secretome libraries, as well as how secretome libraries have been applied to discover novel target biology using different disease-relevant cell-based assays. A workflow for secretome-based screening is shared based on the AstraZeneca experience. The secretome library offers several advantages compared with other libraries used for target discovery: (1) screening using a secretome library directly identifies the active protein and, in many cases, its cognate receptor, enabling a rapid understanding of the disease pathway and subsequent formation of target hypotheses for drug discovery; (2) the secretome library covers significant areas of biological signaling space, although the size of this library is small; (3) secretome proteins can be added directly to cells without additional manipulation. These factors make the secretome library ideal for testing in physiologically relevant cell types, and therefore it represents an attractive approach to phenotypic target discovery.

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.

How polypharmacologic is each chemogenomics library?

Aim:

High-throughput phenotypic screens have emerged as a promising avenue for small-molecule drug discovery. The challenge faced in high-throughput phenotypic screens is target deconvolution once a small molecule hit is identified. Chemogenomics libraries have emerged as an important tool for meeting this challenge. Here, we investigate their target-specificity by deriving a ‘polypharmacology index’ for broad chemogenomics screening libraries.

Methods:

All known targets of all the compounds in each library were plotted as a histogram and fitted to a Boltzmann distribution, whose linearized slope is indicative of the overall polypharmacology of the library.

Results & conclusion:

Comparison of libraries clearly distinguished the most target-specific library, which might be assumed to be more useful for target deconvolution in a phenotypic screen.

Discovery of HSPG2 (Perlecan) as a Therapeutic Target in Triple Negative Breast Cancer

In recent years, there have been significant advances in the treatment of breast cancer resulting in remarkably high survival rates. However, treatment options for metastatic triple negative breast cancer (TNBC) are quite limited due to a lack of identifiable, unique markers. Using a phage display-based whole cell biopanning procedure, we developed two human antibodies that bind to tumor cells with a metastatic TNBC phenotype. Our studies further identified domain 1 of HSPG2 (perlecan) protein as the cognate cell surface antigen bound by the antibody. Immunohistochemistry studies utilizing patient tissue samples revealed significant cell surface expression of HSPG2 in both primary tumors and metastatic lesions. Further, higher HSPG2 expression correlated with poor survival in TNBC. The affinity-matured antibody inhibited the growth of triple negative MDA-MB-231 tumors to a greater extent in nude mice than in NSG mice, pointing to the potential role of natural killer cell-mediated antibody-dependent cell cytotoxicity. This mechanism of action was confirmed through in vitro assays using mouse splenocytes and human peripheral blood mononuclear cells (PBMCs). These results suggest that HSPG2 is a promising target in metastatic TNBC and HSPG2-targeted antibodies could represent a potentially novel class of targeted therapeutics for TNBC.

Deep Mining of Complex Antibody Phage Pools Generated by Cell Panning Enables Discovery of Rare Antibodies Binding New Targets and Epitopes

Phage display technology is a common approach for discovery of therapeutic antibodies. Drug candidates are typically isolated in two steps: First, a pool of antibodies is enriched through consecutive rounds of selection on a target antigen, and then individual clones are characterized in a screening procedure. When whole cells are used as targets, as in phenotypic discovery, the output phage pool typically contains thousands of antibodies, binding, in theory, hundreds of different cell surface receptors. Clonal expansion throughout the phage display enrichment process is affected by multiple factors resulting in extremely complex output phage pools where a few antibodies are highly abundant and the majority is very rare. This is a huge challenge in the screening where only a fraction of the antibodies can be tested using a conventional binding analysis, identifying mainly the most abundant clones typically binding only one or a few targets. As the expected number of antibodies and specificities in the pool is much higher, complementing methods, to reach deeper into the pool, are required, called deep mining methods. In this study, four deep mining methods were evaluated: 1) isolation of rare sub-pools of specific antibodies through selection on recombinant proteins predicted to be expressed on the target cells, 2) isolation of a sub-pool enriched for antibodies of unknown specificities through depletion of the primary phage pool on recombinant proteins corresponding to receptors known to generate many binders, 3) isolation of a sub-pool enriched for antibodies through selection on cells blocked with antibodies dominating the primary phage pool, and 4) next-generation sequencing-based analysis of isolated antibody pools followed by antibody gene synthesis and production of rare but enriched clones. We demonstrate that antibodies binding new targets and epitopes, not discovered through screening alone, can be discovered using described deep mining methods. Overall, we demonstrate the complexity of phage pools generated through selection on cells and show that a combination of conventional screening and deep mining methods are needed to fully utilize such pools. Deep mining will be important in future phenotypic antibody drug discovery efforts to increase the diversity of identified antibodies and targets.

MolTarPred: A web tool for comprehensive target prediction with reliability estimation

Molecular target prediction can provide a starting point to understand the efficacy and side effects of phenotypic screening hits. Unfortunately, the vast majority of in silico target prediction methods are not available as web tools. Furthermore, these are limited in the number of targets that can be predicted, do not estimate which target predictions are more reliable and/or lack comprehensive retrospective validations. We present MolTarPred ( http://moltarpred.marseille.inserm.fr/), a user-friendly web tool for predicting protein targets of small organic compounds. It is powered by a large knowledge base comprising 607,659 compounds and 4,553 macromolecular targets collected from the ChEMBL database. In about 1 min, the predicted targets for the supplied molecule will be listed in a table. The chemical structures of the query molecule and the most similar compounds annotated with the predicted target will also be shown to permit visual inspection and comparison. Practical examples of the use of MolTarPred are showcased. MolTarPred is a new resource for scientists that require a more complete knowledge of the polypharmacology of a molecule. The introduction of a reliability score constitutes an attractive functionality of MolTarPred, as it permits focusing experimental confirmatory tests on the most reliable predictions, which leads to higher prospective hit rates.

Drug Screening Platforms and RPPA

Since its inception as a scalable and cost-effective method for precise quantification of the abundance of multiple protein analytes and post-translational epitopes across large sample sets, reverse phase protein array (RPPA) has been utilized as a drug discovery tool. Key RPPA drug discovery applications include primary screening of abundance or activation state of nominated protein targets, secondary screening for toxicity and selectivity, mechanism-of-action profiling, biomarker discovery, and drug combination discovery. In recent decades, drug discovery strategies have evolved dramatically in response to continual advances in technology platforms supporting high-throughput screening, structure-based drug design, new therapeutic modalities, and increasingly more complex and disease-relevant cell-based and in vivo preclinical models of disease. Advances in biological laboratory capabilities in drug discovery are complemented by significant developments in bioinformatics and computational approaches for integrating large complex datasets. Bioinformatic and computational analysis of integrated molecular, pathway network and phenotypic datasets enhance multiple stages of the drug discovery process and support more informative drug target hypothesis generation and testing. In this chapter we discuss and present examples demonstrating how the latest advances in RPPA complement and integrate with other emerging drug screening platforms to support a new era of more informative and evidence-led drug discovery strategies.

Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers

Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.

Identifying Lysophosphatidic Acid Acyltransferase β (LPAAT-β) as the Target of a Nanomolar Angiogenesis Inhibitor from a Phenotypic Screen Using the Polypharmacology Browser PPB2

By screening a focused library of kinase inhibitor analogues in a phenotypic co-culture assay for angiogenesis inhibition, we identified an aminotriazine that acts as a cytostatic nanomolar inhibitor. However, this aminotriazine was found to be completely inactive in a whole-kinome profiling assay. To decipher its mechanism of action, we used the online target prediction tool PPB2 (http://ppb2.gdb.tools), which suggested lysophosphatidic acid acyltransferase β (LPAAT-β) as a possible target for this aminotriazine as well as several analogues identified by structure-activity relationship profiling. LPAAT-β inhibition (IC₅₀ ≈15 nm) was confirmed in a biochemical assay and by its effects on cell proliferation in comparison with a known LPAAT-β inhibitor. These experiments illustrate the value of target-prediction tools to guide target identification for phenotypic screening hits and significantly expand the rather limited pharmacology of LPAAT-β inhibitors.

Methods to validate Hsp90 inhibitor specificity, to identify off-target effects, and to rethink approaches for further clinical development

The molecular chaperone Hsp90 is one component of a highly complex and interactive cellular proteostasis network (PN) that participates in protein folding, directs misfolded and damaged proteins for destruction, and participates in regulating cellular transcriptional responses to environmental stress, thus promoting cell and organismal survival. Over the last 20 years, it has become clear that various disease states, including cancer, neurodegeneration, metabolic disorders, and infection by diverse microbes, impact the PN. Among PN components, Hsp90 was among the first to be pharmacologically targeted with small molecules. While the number of Hsp90 inhibitors described in the literature has dramatically increased since the first such small molecule was described in 1994, it has become increasingly apparent that not all of these agents have been sufficiently validated for specificity, mechanism of action, and lack of off-target effects. Given the less than expected activity of Hsp90 inhibitors in cancer-related human clinical trials, a re-evaluation of potentially confounding off-target effects, as well as confidence in target specificity and mechanism of action, is warranted. In this commentary, we provide feasible approaches to achieve these goals and we discuss additional considerations to improve the clinical efficacy of Hsp90 inhibitors in treating cancer and other diseases.

Extending in Silico Protein Target Prediction Models to Include Functional Effects

In silico protein target deconvolution is frequently used for mechanism-of-action investigations; however existing protocols usually do not predict compound functional effects, such as activation or inhibition, upon binding to their protein counterparts. This study is hence concerned with including functional effects in target prediction. To this end, we assimilated a bioactivity training set for 332 targets, comprising 817,239 active data points with unknown functional effect (binding data) and 20,761,260 inactive compounds, along with 226,045 activating and 1,032,439 inhibiting data points from functional screens. Chemical space analysis of the data first showed some separation between compound sets (binding and inhibiting compounds were more similar to each other than both binding and activating or activating and inhibiting compounds), providing a rationale for implementing functional prediction models. We employed three different architectures to predict functional response, ranging from simplistic random forest models ('Arch1') to cascaded models which use separate binding and functional effect classification steps ('Arch2' and 'Arch3'), differing in the way training sets were generated. Fivefold stratified cross-validation outlined cascading predictions provides superior precision and recall based on an internal test set. We next prospectively validated the architectures using a temporal set of 153,467 of in-house data points (after a 4-month interim from initial data extraction). Results outlined Arch3 performed with the highest target class averaged precision and recall scores of 71% and 53%, which we attribute to the use of inactive background sets. Distance-based applicability domain (AD) analysis outlined that Arch3 provides superior extrapolation into novel areas of chemical space, and thus based on the results presented here, propose as the most suitable architecture for the functional effect prediction of small molecules. We finally conclude including functional effects could provide vital insight in future studies, to annotate cases of unanticipated functional changeover, as outlined by our CHRM1 case study.

Anticancer Pyrroloquinazoline LBL1 Targets Nuclear Lamins

Target identification of bioactive compounds is critical for understanding their mechanism of action. We previously discovered a novel pyrroloquinazoline compound LBL1 with significant anticancer activity. However, its molecular targets remain to be established. Herein, we developed a clickable photoaffinity probe based on LBL1. Using extensive chemical, biochemical, and cellular studies with this probe and LBL1, we found that LBL1 targets nuclear lamins, which are type V intermediate filament (IF) proteins. Further studies showed that LBL1 binds to the coiled-coil domain of lamin A. These results revealed that IF proteins can also be targeted with appropriate small molecules besides two other cytoskeletal proteins actin filaments and microtubules, providing a novel avenue to investigate lamin biology and a novel strategy to develop distinct anticancer therapies.

In silico profiling of systemic effects of drugs to predict unexpected interactions

Identifying unexpected drug interactions is an essential step in drug development. Most studies focus on predicting whether a drug pair interacts or is effective on a certain disease without considering the mechanism of action (MoA). Here, we introduce a novel method to infer effects and interactions of drug pairs with MoA based on the profiling of systemic effects of drugs. By investigating propagated drug effects from the molecular and phenotypic networks, we constructed profiles of 5,441 approved and investigational drugs for 3,833 phenotypes. Our analysis indicates that highly connected phenotypes between drug profiles represent the potential effects of drug pairs and the drug pairs with strong potential effects are more likely to interact. When applied to drug interactions with verified effects, both therapeutic and adverse effects have been successfully identified with high specificity and sensitivity. Finally, tracing drug interactions in molecular and phenotypic networks allows us to understand the MoA.

Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server

Background

The identification of target molecules is important for understanding the mechanism of “target deconvolution” in phenotypic screening and “polypharmacology” of drugs. Because conventional methods of identifying targets require time and cost, in-silico target identification has been considered an alternative solution. One of the well-known in-silico methods of identifying targets involves structure activity relationships (SARs). SARs have advantages such as low computational cost and high feasibility; however, the data dependency in the SAR approach causes imbalance of active data and ambiguity of inactive data throughout targets.

Results

We developed a ligand-based virtual screening model comprising 1121 target SAR models built using a random forest algorithm. The performance of each target model was tested by employing the ROC curve and the mean score using an internal five-fold cross validation. Moreover, recall rates for top-k targets were calculated to assess the performance of target ranking. A benchmark model using an optimized sampling method and parameters was examined via external validation set. The result shows recall rates of 67.6% and 73.9% for top-11 (1% of the total targets) and top-33, respectively. We provide a website for users to search the top-k targets for query ligands available publicly at http://rfqsar.kaist.ac.kr.

Conclusions

The target models that we built can be used for both predicting the activity of ligands toward each target and ranking candidate targets for a query ligand using a unified scoring scheme. The scores are additionally fitted to the probability so that users can estimate how likely a ligand–target interaction is active. The user interface of our web site is user friendly and intuitive, offering useful information and cross references.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1960-x) contains supplementary material, which is available to authorized users.

Development of the Theta Comparative Cell Scoring Method to Quantify Diverse Phenotypic Responses Between Distinct Cell Types

In this article, we have developed novel data visualization tools and a Theta comparative cell scoring (TCCS) method, which supports high-throughput in vitro pharmacogenomic studies across diverse cellular phenotypes measured by multiparametric high-content analysis. The TCCS method provides a univariate descriptor of divergent compound-induced phenotypic responses between distinct cell types, which can be used for correlation with genetic, epigenetic, and proteomic datasets to support the identification of biomarkers and further elucidate drug mechanism-of-action. Application of these methods to compound profiling across high-content assays incorporating well-characterized cells representing known molecular subtypes of disease supports the development of personalized healthcare strategies without prior knowledge of a drug target. We present proof-of-principle data quantifying distinct phenotypic response between eight breast cancer cells representing four disease subclasses. Application of the TCCS method together with new advances in next-generation sequencing, induced pluripotent stem cell technology, gene editing, and high-content phenotypic screening are well placed to advance the identification of predictive biomarkers and personalized medicine approaches across a broader range of disease types and therapeutic classes.

How Phenotypic Screening Influenced Drug Discovery: Lessons from Five Years of Practice

Since 2011, phenotypic screening has been a trend in the pharmaceutical industry as well as in academia. This renaissance was triggered by analyses that suggested that phenotypic screening is a superior strategy to discover first-in-class drugs. Despite these promises and considerable investments, pharmaceutical research organizations have encountered considerable challenges with the approach. Few success stories have emerged in the past 5 years and companies are questioning their investment in this area. In this contribution, we outline what we have learned about success factors and challenges of phenotypic screening. We then describe how our efforts in phenotypic screening have influenced our approach to drug discovery in general. We predict that concepts from phenotypic screening will be incorporated into target-based approaches and will thus remain influential beyond the current trend.

FD5180, a Novel Protein Kinase Affinity Probe, and the Effect of Bead Loading on Protein Kinase Identification

The effects of compound loading on the identification of protein kinases (PKs) was examined using two previously reported sepharose-supported PK inhibitors (PKIs): bisindolylmaleimide X (S1) and CZC8004 (S2). Compound loadings of 0.1, 0.5, 2.5, 5, 10, 25, and 50% content and an ethanolamine-blocked control bead (no compound) were investigated. A 50% bead loading gave the highest level of PK identification for both S1 and S2, extracting 34 and 55 PKs, respectively, from a single cell lysate. Control beads allowed overall identification of 23 PKs, which we term the kinase beadome, whereas sepharose-supported sunitinib (S7; 50% loading) identified 20, 11 of which were common to the control beads. The reliability of bead pull-downs was examined in duplicate experiments using two independently synthesized batches each of S1 and S2. Bead S1 showed high similarity in the absolute numbers of PKs identified across two experiments, at 40 and 35 PKs, of which 26 were common across the two batches of beads, with 14 and 9 unique PKs identified in each experiment. The S2 beads extracted 61 and 64 PKs with 55 PKs common across the two bead batches examined. We also report on the development and use of a novel promiscuous PKI analogue, 2-[(5-chloro-2{[4-(piperazin-1-yl)phenyl]amino}pyrimidin-4-yl)amino]-N-methylbenzene-sulfonamide (S15), which extracted 12 additional unique PKs over the two parent compounds from which it was designed, the combination of which identifies 160 unique PKs. S15 was based on the common pyrimidine core scaffold of S9 and S10. Thus, S15 expands the utility of kinobeads by broadening the kinome coverage for bead-based pull-down. Combining the data for all beads across 90 and 180 min liquid chromatography-mass spectrometry (LC-MS)/MS analysis identified a total of 160 unique PKs.

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.