Emerging Approaches for the Identification of Protein Targets of Small Molecules - A Practitioners’ Perspective

Illuminating Dark Chemical Matter Using the Cell Painting Assay

Screening for small-molecule modulators of disease-relevant targets and phenotypes is the first step on the way to new drugs. Large compound libraries have been synthesized by academia and, particularly, pharmaceutical companies to meet the need for novel chemical entities that are as diverse as possible. Screening of these compound libraries revealed a portion of small molecules that is inactive in more than 100 different assays and was therefore termed "dark chemical matter" (DCM). Deorphanization of DCM promises to yield very selective compounds as they are expected to have less off-target effects. We employed morphological profiling using the Cell Painting assay to detect bioactive DCM. Within the DCM collection, we identified bioactive compounds and confirmed several modulators of microtubules, DNA synthesis, and pyrimidine biosynthesis. Profiling approaches are, therefore, powerful tools to probe compound collections for bioactivity in an unbiased manner and are particularly suitable for deorphanization of DCM.

Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning

Estimating protein targets of compounds based on the similarity principle-similar molecules are likely to show comparable bioactivity-is a long-standing strategy in drug research. Having previously quantified this principle, we present here a large-scale evaluation of its predictive power for inferring macromolecular targets by reverse screening an unprecedented vast external test set of more than 300,000 active small molecules against another bioactivity set of more than 500,000 compounds. We show that machine-learning can predict the correct targets, with the highest probability among 2069 proteins, for more than 51% of the external molecules. The strong enrichment thus obtained demonstrates its usefulness in supporting phenotypic screens, polypharmacology, or repurposing. Moreover, we quantified the impact of the bioactivity knowledge available for proteins in terms of number and diversity of actives. Finally, we advise that developers of such approaches follow an application-oriented benchmarking strategy and use large, high-quality, non-overlapping datasets as provided here.

Experimental structure based drug design (SBDD) applications for anti-leishmanial drugs: A paradigm shift?

Leishmaniasis is a group of neglected tropical diseases caused by at least 20 species of Leishmania protozoa, which are spread by the bite of infected sandflies. There are three main forms of the disease: cutaneous leishmaniasis (CL, the most common), visceral leishmaniasis (VL, also known as kala-azar, the most serious), and mucocutaneous leishmaniasis. One billion people live in areas endemic to leishmaniasis, with an annual estimation of 30,000 new cases of VL and more than 1 million of CL. New treatments for leishmaniasis are an urgent need, as the existing ones are inefficient, toxic, and/or expensive. We have revised the experimental structure-based drug design (SBDD) efforts applied to the discovery of new drugs against leishmaniasis. We have grouped the explored targets according to the metabolic pathways they belong to, and the key achieved advances are highlighted and evaluated. In most cases, SBDD studies follow high-throughput screening campaigns and are secondary to pharmacokinetic optimization, due to the majoritarian belief that there are few validated targets for SBDD in leishmaniasis. However, some SBDD strategies have significantly contributed to new drug candidates against leishmaniasis and a bigger number holds promise for future development.

PT-Finder: A multi-modal neural network approach to target identification

Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.

Experimental and data analysis advances in thermal proteome profiling

Method development for mass spectrometry (MS)-based thermal shift proteomic assays have advanced to probe small molecules with known and unknown protein-ligand interaction mechanisms and specificity, which is predominantly used in characterization of drug-protein interactions. In the discovery of target and off-target protein-ligand interactions, a thorough investigation of method development and their impact on the sensitivity and accuracy of protein-small molecule and protein-protein interactions is warranted. In this review, we discuss areas of improvement at each stage of thermal proteome profiling data analysis that includes processing of MS-based data, method development, and their effect on the overall quality of thermal proteome profiles. We also overview the optimization of experimental strategies and prioritization of an increased number of independent biological replicates over the number of evaluated temperatures.

Identification of Low-Density Lipoprotein Receptor-Related Protein 1 as a CXCL14 Receptor Using Chemically Synthesized Tetrafunctional Probes

CXCL14 is a primordial CXC-type chemokine that transports CpG oligodeoxynucleotides (ODN) into endosomes and lysosomes in dendritic cells, thereby leading to the activation of the Toll-like receptor 9 (TLR9)-mediated innate immune system. However, the underlying molecular mechanism by which the CXCL14-CpG ODN complex enters cells remains elusive. Herein, we describe the chemical synthesis of CXCL14-derived photoaffinity probes and their application to the identification of target receptors for CXCL14 using quantitative proteomics. By utilizing native chemical ligation and maleimide-thiol coupling chemistry, we synthesized site-specifically modified CXCL14-based photoaffinity probes that contain photoreactive 2-aryl-5-carboxytetrazole (ACT) and a hydrazine-labile cleavable linker. CXCL14-based probes were found to be capable of binding CpG ODN to immune cells, whose bioactivities were comparable to native CXCL14. Application of CXCL14-derived probes to quantitative proteomic experiments enabled the identification of dozens of target receptor candidates for CXCL14 in mouse macrophage-derived RAW264.7 cells, and we discovered that low-density lipoprotein receptor-related protein 1 (LRP1) is a novel receptor for CXCL14 by competitive proteome profiling. We further showed that disruption of LRP1 affected the incorporation of the CXCL14-CpG ODN complex in the cells. Overall, this report highlights the power of synthetic CXCL14-derived photoaffinity probes combined with chemical proteomics to discover previously unidentified receptors for CXCL14, which could promote an understanding of the molecular functions of CXCL14 and the elaborate machinery of innate immune systems.

Drug upgrade: A complete methodology from old drug to new chemical entities using Nematic Protein Organization Technique

Drug repurposing is used to propose new therapeutic perspectives. Here, we introduce "Drug Upgrade", that is, characterizing the mode of action of an old drug to generate new chemical entities and new therapeutics. We proposed a novel methodology covering target identification to pharmacology validation. As an old drug, we chose hydroxychloroquine (HCQ) for its well-documented clinical efficacy in lupus and its side effect, retinal toxicity. Using the Nematic Protein Organization Technique (NPOT®) followed by liquid chromatography-tandem mass spectrometry analyses, we identified myeloperoxidase (MPO) and alpha-crystallin β chain (CRYAB) as primary and secondary targets to HCQ from lupus patients' peripheral blood mononuclear cells (PBMCs) and isolated human retinas. Surface plasmon resonance (SPR) and enzymatic assays confirmed the interaction of HCQ with MPO and CRYAB. We synthesized INS-072 a novel analog of HCQ that increased affinity for MPO and decreased binding to CRYAB compared to HCQ. INS-072 delayed cutaneous eruption significantly compared to HCQ in the murine MRL/lpr model of spontaneous lupus and prevents immune complex vasculitis in mice. In addition, long-term HCQ treatment caused retinal toxicity in mice, unlike INS-072. Our study illustrates a method of drug development, where new applications or improvements can be explored by fully characterizing the drug's mode of action.

Inducible mismatch repair streamlines forward genetic approaches to target identification of cytotoxic small molecules

Orphan cytotoxins are small molecules for which the mechanism of action (MoA) is either unknown or ambiguous. Unveiling the mechanism of these compounds may lead to useful tools for biological investigation and new therapeutic leads. In selected cases, the DNA mismatch repair-deficient colorectal cancer cell line, HCT116, has been used as a tool in forward genetic screens to identify compound-resistant mutations, which have ultimately led to target identification. To expand the utility of this approach, we engineered cancer cell lines with inducible mismatch repair deficits, thus providing temporal control over mutagenesis. By screening for compound resistance phenotypes in cells with low or high rates of mutagenesis, we increased both the specificity and sensitivity of identifying resistance mutations. Using this inducible mutagenesis system, we implicate targets for multiple orphan cytotoxins, including a natural product and compounds emerging from a high-throughput screen, thus providing a robust tool for future MoA studies.

Challenges and Perspectives in Target Identification and Mechanism Illustration for Chinese Medicine

Chinese medicine (CM) is an important resource for human life understanding and discovery of drugs. However, due to the unclear pharmacological mechanism caused by unclear target, research and international promotion of many active components have made little progress in the past decades of years. CM is mainly composed of multi-ingredients with multi-targets. The identification of targets of multiple active components and the weight analysis of multiple targets in a specific pathological environment, that is, the determination of the most important target is the main obstacle to the mechanism clarification and thus hinders its internationalization. In this review, the main approach to target identification and network pharmacology were summarized. And BIBm (Bayesian inference modeling), a powerful method for drug target identification and key pathway determination was introduced. We aim to provide a new scientific basis and ideas for the development and international promotion of new drugs based on CM.

Inducible mismatch repair streamlines forward genetic approaches to target identification of cytotoxic small molecules

Orphan cytotoxins are small molecules for which the mechanism of action (MoA) is either unknown or ambiguous. Unveiling the mechanism of these compounds may lead to useful tools for biological investigation and in some cases, new therapeutic leads. In select cases, the DNA mismatch repair-deficient colorectal cancer cell line, HCT116, has been used as a tool in forward genetic screens to identify compound-resistant mutations, which have ultimately led to target identification. To expand the utility of this approach, we engineered cancer cell lines with inducible mismatch repair deficits, thus providing temporal control over mutagenesis. By screening for compound resistance phenotypes in cells with low or high rates of mutagenesis, we increased both the specificity and sensitivity of identifying resistance mutations. Using this inducible mutagenesis system, we implicate targets for multiple orphan cytotoxins, including a natural product and compounds emerging from a high-throughput screen, thus providing a robust tool for future MoA studies.

Discovery of Clinical Candidate AZD0095, a Selective Inhibitor of Monocarboxylate Transporter 4 (MCT4) for Oncology

Due to increased reliance on glycolysis, which produces lactate, monocarboxylate transporters (MCTs) are often upregulated in cancer. MCT4 is associated with the export of lactic acid from cancer cells under hypoxia, so inhibition of MCT4 may lead to cytotoxic levels of intracellular lactate. In addition, tumor-derived lactate is known to be immunosuppressive, so MCT4 inhibition may be of interest for immuno-oncology. At the outset, no potent and selective MCT4 inhibitors had been reported, but a screen identified a triazolopyrimidine hit, with no close structural analogues. Minor modifications to the triazolopyrimidine were made, alongside design of a constrained linker and broad SAR exploration of the biaryl tail to improve potency, physical properties, PK, and hERG. The resulting clinical candidate 15 (AZD0095) has excellent potency (1.3 nM), MCT1 selectivity (>1000×), secondary pharmacology, clean mechanism of action, suitable properties for oral administration in the clinic, and good preclinical efficacy in combination with cediranib.

3-phenacylideneoxindoles as a new class of antifungal compounds against Paracoccidioides spp

Aims: Considering the need to identify new compounds with antifungal action, the activity of five 3-phenacylideneoxindoles compounds was evaluated. Materials & methods: The compounds were synthesized, and their antifungal activity was elucidated through minimum inhibitory concentration tests and interaction assay with other antifungals. Potential targets of compounds were predicted in silico. Results: 3-phenacylideneoxindoles compounds inhibited fungal growth with minimum inhibitory concentration and minimum fungicidal concentration ranging from 3.05 to 12.26 μM. The compounds demonstrated high selectivity index and presented a synergistic effect with itraconazole. In silico prediction revealed the pentafunctional AROM polypeptide, enolase, superoxide dismutase, catalase and kinases as proteins targets of the compound 4a. Conclusion: The results demonstrate that 3-phenacylideneoxindoles is a potential new class of antifungal compounds for paracoccidioidomycosis treatment.

Small molecule photocatalysis enables drug target identification via energy transfer

Over half of new therapeutic approaches fail in clinical trials due to a lack of target validation. As such, the development of new methods to improve and accelerate the identification of cellular targets, broadly known as target ID, remains a fundamental goal in drug discovery. While advances in sequencing and mass spectrometry technologies have revolutionized drug target ID in recent decades, the corresponding chemical-based approaches have not changed in over 50 y. Consigned to outdated stoichiometric activation modes, modern target ID campaigns are regularly confounded by poor signal-to-noise resulting from limited receptor occupancy and low crosslinking yields, especially when targeting low abundance membrane proteins or multiple protein target engagement. Here, we describe a broadly general platform for photocatalytic small molecule target ID, which is founded upon the catalytic amplification of target-tag crosslinking through the continuous generation of high-energy carbene intermediates via visible light-mediated Dexter energy transfer. By decoupling the reactive warhead tag from the small molecule ligand, catalytic signal amplification results in unprecedented levels of target enrichment, enabling the quantitative target and off target ID of several drugs including (+)-JQ1, paclitaxel (Taxol), dasatinib (Sprycel), as well as two G-protein-coupled receptors-ADORA2A and GPR40.

Morphological profiling by means of the Cell Painting assay enables identification of tubulin-targeting compounds

In phenotypic compound discovery, conclusive identification of cellular targets and mode of action are often impaired by off-target binding. In particular, microtubules are frequently targeted in cellular assays. However, in vitro tubulin binding assays do not correctly reflect the cellular context, and conclusive high-throughput phenotypic assays monitoring tubulin binding are scarce, such that tubulin binding is rarely identified. We report that morphological profiling using the Cell Painting assay (CPA) can efficiently detect tubulin modulators in compound collections with a high throughput, including annotated reference compounds and unannotated compound classes with unrelated chemotypes and scaffolds. Small-molecule tubulin binders share similar CPA fingerprints, which enables prediction and experimental validation of microtubule-binding activity. Our findings suggest that CPA or a related morphological profiling approach will be an invaluable addition to small-molecule discovery programs in chemical biology and medicinal chemistry, enabling early identification of one of the most frequently observed off-target activities.

One-Step Synthesis of Photoaffinity Probes for Live-Cell MS-Based Proteomics

We present a one-step Ugi reaction protocol for the expedient synthesis of photoaffinity probes for live-cell MS-based proteomics. The reaction couples an amine affinity function with commonly used photoreactive groups, and a variety of handle functionalities. Using this technology, a series of pan-BET (BET: bromodomain and extra-terminal domain) selective bromodomain photoaffinity probes were obtained by parallel synthesis. Studies on the effects of photoreactive group, linker length and irradiation wavelength on photocrosslinking efficiency provide valuable insights into photoaffinity probe design. Optimal probes were progressed to MS-based proteomics to capture the BET family of proteins from live cells and reveal their potential on- and off-target profiles.

In vitro and in silico analysis reveals antifungal activity and potential targets of curcumin on Paracoccidioides spp

The search for new compounds with activity against Paracoccidioides, etiologic agents of Paracoccidioidomycosis (PCM), is extremely necessary due to the current scenario of the available therapeutic arsenal. Treatment is restricted to three classes of antifungals with side effects. Curcumin is a polyphenol with antifungal effects that is extracted from Curcuma longa. The present work aimed to evaluate the activity of curcumin in different species of Paracoccidioides and to evaluate the potential molecular targets of curcumin using computational strategies. In addition, interactions with classic antifungals used in the treatment of PCM were evaluated. Curcumin inhibits the growth of Paracoccidioides spp. exerting a fungicidal effect. The combination of curcumin with amphotericin B, co-trimoxazole, and itraconazole showed a synergistic or additive interaction. Molecular targets as superoxide dismutase, catalase, and isocitrate lyase were proposed based on in silico approaches. Curcumin affects the fungal plasma membrane and increases the production of reactive oxygen species. Therefore, curcumin is a good alternative for the treatment of PCM.

Molecular Design in Practice: A Review of Selected Projects in a French Research Institute That Illustrates the Link between Chemical Biology and Medicinal Chemistry

Chemical biology and drug discovery are two scientific activities that pursue different goals but complement each other. The former is an interventional science that aims at understanding living systems through the modulation of its molecular components with compounds designed for this purpose. The latter is the art of designing drug candidates, i.e., molecules that act on selected molecular components of human beings and display, as a candidate treatment, the best reachable risk benefit ratio. In chemical biology, the compound is the means to understand biology, whereas in drug discovery, the compound is the goal. The toolbox they share includes biological and chemical analytic technologies, cell and whole-body imaging, and exploring the chemical space through state-of-the-art design and synthesis tools. In this article, we examine several tools shared by drug discovery and chemical biology through selected examples taken from research projects conducted in our institute in the last decade. These examples illustrate the design of chemical probes and tools to identify and validate new targets, to quantify target engagement in vitro and in vivo, to discover hits and to optimize pharmacokinetic properties with the control of compound concentration both spatially and temporally in the various biophases of a biological system.

Alleviation of a polyglucosan storage disorder by enhancement of autophagic glycogen catabolism

This work employs adult polyglucosan body disease (APBD) models to explore the efficacy and mechanism of action of the polyglucosan-reducing compound 144DG11. APBD is a glycogen storage disorder (GSD) caused by glycogen branching enzyme (GBE) deficiency causing accumulation of poorly branched glycogen inclusions called polyglucosans. 144DG11 improved survival and motor parameters in a GBE knockin (Gbeys/ys ) APBD mouse model. 144DG11 reduced polyglucosan and glycogen in brain, liver, heart, and peripheral nerve. Indirect calorimetry experiments revealed that 144DG11 increases carbohydrate burn at the expense of fat burn, suggesting metabolic mobilization of pathogenic polyglucosan. At the cellular level, 144DG11 increased glycolytic, mitochondrial, and total ATP production. The molecular target of 144DG11 is the lysosomal membrane protein LAMP1, whose interaction with the compound, similar to LAMP1 knockdown, enhanced autolysosomal degradation of glycogen and lysosomal acidification. 144DG11 also enhanced mitochondrial activity and modulated lysosomal features as revealed by bioenergetic, image-based phenotyping and proteomics analyses. As an effective lysosomal targeting therapy in a GSD model, 144DG11 could be developed into a safe and efficacious glycogen and lysosomal storage disease therapy.

Proteomics in the pharmaceutical and biotechnology industry: a look to the next decade

INTRODUCTION

Pioneering technologies such as proteomics have helped fuel the biotechnology and pharmaceutical industry with the discovery of novel targets and an intricate understanding of the activity of therapeutics and their various activities in vitro and in vivo. The field of proteomics is undergoing an inflection point, where new sensitive technologies are allowing intricate biological pathways to be better understood, and novel biochemical tools are pivoting us into a new era of chemical proteomics and biomarker discovery. In this review, we describe these areas of innovation, and discuss where the fields are headed in terms of fueling biotechnological and pharmacological research and discuss current gaps in the proteomic technology landscape.

AREAS COVERED

Single cell sequencing and single molecule sequencing. Chemoproteomics. Biological matrices and clinical samples including biomarkers. Computational tools including instrument control software, data analysis.

EXPERT OPINION

Proteomics will likely remain a key technology in the coming decade, but will have to evolve with respect to type and granularity of data, cost and throughput of data generation as well as integration with other technologies to fulfill its promise in drug discovery.

Discovery and mechanism of action studies of 4,6-diphenylpyrimidine-2-carbohydrazides as utrophin modulators for the treatment of Duchenne muscular dystrophy

Duchenne muscular dystrophy is a fatal disease with no cure, caused by lack of the cytoskeletal protein dystrophin. Upregulation of utrophin, a dystrophin paralogue, offers a potential therapy independent of mutation type. The failure of first-in-class utrophin modulator ezutromid/SMT C1100 in Phase II clinical trials necessitates development of compounds with better efficacy, physicochemical and ADME properties and/or complementary mechanisms. We have discovered and performed a preliminary optimisation of a novel class of utrophin modulators using an improved phenotypic screen, where reporter expression is derived from the full genomic context of the utrophin promoter. We further demonstrate through target deconvolution studies, including expression analysis and chemical proteomics, that this compound series operates via a novel mechanism of action, distinct from that of ezutromid.

High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need

Conventional thinking in modern drug discovery postulates that the design of highly selective molecules which act on a single disease-associated target will yield safer and more effective drugs. However, high clinical attrition rates and the lack of progress in developing new effective treatments for many important diseases of unmet therapeutic need challenge this hypothesis. This assumption also impinges upon the efficiency of target agnostic phenotypic drug discovery strategies, where early target deconvolution is seen as a critical step to progress phenotypic hits. In this review we provide an overview of how emerging phenotypic and pathway-profiling technologies integrate to deconvolute the mechanism-of-action of phenotypic hits. We propose that such in-depth mechanistic profiling may support more efficient phenotypic drug discovery strategies that are designed to more appropriately address complex heterogeneous diseases of unmet need.

Streamlined Target Deconvolution Approach Utilizing a Single Photoreactive Chloroalkane Capture Tag

Identification of physiologically relevant targets for lead compounds emerging from drug discovery screens is often the rate-limiting step toward understanding their mechanism of action and potential for undesired off-target effects. To this end, we developed a streamlined chemical proteomic approach utilizing a single, photoreactive cleavable chloroalkane capture tag, which upon attachment to bioactive compounds facilitates selective isolation of their respective cellular targets for subsequent identification by mass spectrometry. When properly positioned, the tag does not significantly affect compound potency and membrane permeability, allowing for binding interactions with the tethered compound (probe) to be established within intact cells under physiological conditions. Subsequent UV-induced covalent photo-cross-linking "freezes" the interactions between the probe and its cellular targets and prevents their dissociation upon cell lysis. Targets cross-linked to the capture tag are then efficiently enriched through covalent capture onto HaloTag coated beads and subsequent selective chemical release from the solid support. The tag's built-in capability for selective enrichment eliminates the need for ligation of a capture tag, thereby simplifying the workflow and reducing variability introduced through additional operational steps. At the same time, the capacity for adequate cross-linking without structural optimization permits modular assembly of photoreactive chloroalkane probes, which reduces the burden of customized chemistry. Using three model compounds, we demonstrate the capability of this approach to identify known and novel cellular targets, including those with low affinity and/or low abundance as well as membrane targets with several transmembrane domains.

Chemoproteomic-enabled phenotypic screening

The ID of disease-modifying, chemically accessible targets remains a central priority of modern therapeutic discovery. The phenotypic screening of small-molecule libraries not only represents an attractive approach to identify compounds that may serve as drug leads but also serves as an opportunity to uncover compounds with novel mechanisms of action (MoAs). However, a major bottleneck of phenotypic screens continues to be the ID of pharmacologically relevant target(s) for compounds of interest. The field of chemoproteomics aims to map proteome-wide small-molecule interactions in complex, native systems, and has proved a key technology to unravel the protein targets of pharmacological modulators. In this review, we discuss the application of modern chemoproteomic methods to identify protein targets of phenotypic screening hits and investigate MoAs, with a specific focus on the development of chemoproteomic-enabled compound libraries to streamline target discovery.

From genomes to molecular dynamics - A bottom up approach in extrication of SARS CoV-2 main protease inhibitors

The recent pandemic Coronavirus disease-19 outbreak had traumatized global countries since its origin in late December 2019. Though the virus originated in China, it has spread rapidly across the world due its firmly established community transmission. To successfully tackle the spread and further infection, there needs a clear multidimensional understanding of the molecular mechanisms. Henceforth, 942 viral genome sequences were analysed to predict the core genomes crucial in virus life cycle. Additionally, 35 small interfering RNA transcripts were predicted that can target specifically the viral core proteins and reduce pathogenesis. The crystal structure of Covid-19 main protease-6LU7 was chosen as an attractive target due to the factors that there were fewer mutations and whose structure had significant identity to the annotated protein sequence of the core genome. Drug repurposing of both recruiting and non recruiting drugs was carried out through molecular docking procedures to recognize bitolterol as a good inhibitor of Covid-19 protease. The study was extended further to screen antiviral phytocompounds through quantitative structure activity relationship and molecular docking to identify davidigenin, from licorice as the best novel lead with good interactions and binding energy. The docking of the best compounds in all three categories was validated with molecular dynamics simulations which implied stable binding of the drug and lead molecule. Though the studies need clinical evaluations, the results are suggestive of curbing the pandemic.

Synthesis, biological evaluation, and molecular docking analysis of phenstatin based indole linked chalcones as anticancer agents and tubulin polymerization inhibitors

A library of new phenstatin based indole linked chalcone compounds (9a-z and 9aa-ad) were designed and synthesized. Of these, compound 9a with 1-methyl, 2- and 3-methoxy substituents in the aromatic ring was efficacious against the human oral cancer cell line SCC-29B, spheroids, and in a mouse xenograft model of oral cancer AW13516. Compound 9a exhibited anti-cancer activity through disrupting cellular integrity and affecting glucose metabolism-which is a hallmark of cancer. The cellular architecture was affected by inhibition of tubulin polymerization as observed by an immunofluorescence assay on 9a-treated SCC-29B cells. An in vitro tubulin polymerization kinetics assay provided evidence of direct interaction of 9a with tubulin. This physical interaction between tubulin and compound 9a was further confirmed by Surface Plasmon Resonance (SPR) analysis. Molecular docking experiments and validations revealed that compound 9a interacts and binds at the colchicine binding site of tubulin and at active sites of key enzymes in the glucose metabolism pathway. Based on in silico modeling, biophysical interactions, and pre-clinical observations, 9a consisting of phenstatin based indole-chalcone scaffolds, can be considered as an attractive tubulin polymerization inhibitor candidate for developing anti-cancer therapeutics.

Handling the Hurdles on the Way to Anti-tuberculosis Drug Development

The global epidemic of tuberculosis (TB) imposes a sustained epidemiologic vigilance and investments in research by governments. Mycobacterium tuberculosis, the main causative agent of TB in human beings, is a very successful pathogen, being the main cause of death in the population among infectious agents. In 2018, ~10 million individuals were contaminated with this bacillus and became ill with TB, and about 1.2 million succumbed to the disease. Most of the success of the M. tuberculosis to linger in the population comes from its ability to persist in an asymptomatic latent state into the host and, in fact, the majority of the individuals are unaware of being contaminated. Even though TB is a treatable disease and is curable in most cases, the treatment is lengthy and laborious. In addition, the rise of resistance to first-line anti-TB drugs elicits a response from TB research groups to discover new chemical entities, preferably with novel mechanisms of action. The pathway to find a new TB drug, however, is arduous and has many barriers that are difficult to overcome. Fortunately, several approaches are available today to be pursued by scientists interested in anti-TB drug development, which goes from massively testing chemical compounds against mycobacteria, to discovering new molecular targets by genetic manipulation. This review presents some difficulties found along the TB drug development process and illustrates different approaches that might be used to try to identify new molecules or targets that are able to impair M. tuberculosis survival.

Photoaffinity Labeling and Quantitative Chemical Proteomics Identify LXRβ as the Functional Target of Enhancers of Astrocytic apoE

Utilizing a phenotypic screen, we identified chemical matter that increased astrocytic apoE secretion in vitro. We designed a clickable photoaffinity probe based on a pyrrolidine lead compound and carried out probe-based quantitative chemical proteomics in human astrocytoma CCF-STTG1 cells to identify liver x receptor β (LXRβ) as the target. Binding of the small molecule ligand stabilized LXRβ, as shown by cellular thermal shift assay (CETSA). In addition, we identified a probe-modified peptide by mass spectrometry and proposed a model where the photoaffinity probe is bound in the ligand-binding pocket of LXRβ. Taken together, our findings demonstrated that the lead chemical matter bound directly to LXRβ, and our results highlight the power of chemical proteomic approaches to identify the target of a phenotypic screening hit. Additionally, the LXR photoaffinity probe and lead compound described herein may serve as valuable tools to further evaluate the LXR pathway.

Detectives and helpers: Natural products as resources for chemical probes and compound libraries

About 70% of the drugs in use are derived from natural products, either used directly or in chemically modified form. Among all possible small molecules (not greater than 5 kDa), only a few of them are biologically active. Natural product libraries may have a higher rate of finding "hits" than synthetic libraries, even with the use of fewer compounds. This is due to the complementarity between the "chemical space" of small molecules and biological macromolecules such as proteins, DNA and RNA, in addition to the three-dimensional complexity of NPs. Chemical probes are molecules which aid in the elucidation of the biological mechanisms behind the action of drugs or drug-like molecules by binding with macromolecular/cellular interaction partners. Probe development and application have been spurred by advancements in photoaffinity label synthesis, affinity chromatography, activity based protein profiling (ABPP) and instrumental methods such as cellular thermal shift assay (CETSA) and advanced/hyphenated mass spectrometry (MS) techniques, as well as genome sequencing and bioengineering technologies. In this review, we restrict ourselves to a survey of natural products (including peptides/mini-proteins and excluding antibodies), which have been applied largely in the last 5 years for the target identification of drugs/drug-like molecules used in research on infectious diseases, and the description of their mechanisms of action.

Combining experimental strategies for successful target deconvolution

Investment in phenotypic drug discovery has led to increased demand for rapid and robust target deconvolution to aid successful drug development. Although methods for target identification and mechanism of action (MoA) discovery are flourishing, they typically lead to lists of putative targets. Validating which target(s) are involved in the therapeutic mechanism of a compound poses a significant challenge, requiring direct binding, target engagement, and functional studies in relevant physiological contexts. A combination of orthogonal approaches can allow target identification beyond the proteome as well as aid prioritisation for resource-intensive target validation studies.

Novel Tetrafunctional Probes Identify Target Receptors and Binding Sites of Small-Molecule Drugs from Living Systems

Significant advancement of chemoproteomics has contributed to uncovering the mechanism of action (MoA) of small-molecule drugs by characterizing drug-protein interactions in living systems. However, cell-membrane proteins such as G protein-coupled receptors (GPCRs) and ion channels, due to their low abundance and unique biophysical properties associated with multiple transmembrane domains, can present challenges for proteome-wide mapping of drug-receptor interactions. Herein, we describe the development of novel tetrafunctional probes, consisting of (1) a ligand of interest, (2) 2-aryl-5-carboxytetrazole (ACT) as a photoreactive group, (3) a hydrazine-labile cleavable linker, and (4) biotin for enrichment. In live cell labeling studies, we demonstrated that the ACT-based probe showed superior reactivity and selectivity for labeling on-target GPCR by mass spectrometry analysis compared with control probes including diazirine-based probes. By leveraging ACT-based cleavable probes, we further identified a set of representative ionotropic receptors, targeted by CNS drugs, with remarkable selectivity and precise binding site information from mouse brain slices. We anticipate that the robust chemoproteomic platform using the ACT-based cleavable probe coupled with phenotypic screening should promote identification of pharmacologically relevant target receptors of drug candidates and ultimately development of first-in-class drugs with novel MoA.

Design and Development of Small-Molecule Arylaldoxime/5-Nitroimidazole Hybrids as Potent Inhibitors of MARK4: A Promising Approach for Target-Based Cancer Therapy

Microtubule affinity-regulating kinase 4 (MARK4), a member of the serine/threonine kinase family, is an emerging therapeutic target in anticancer drug discovery paradigm due to its involvement in regulation of microtubule dynamics, cell cycle regulation, and cancer progression. Therefore, to identify the novel chemical architecture for the design and development of novel MARK4 inhibitors with concomitant radical scavenging property, a series of small-molecule arylaldoxime/5-nitroimidazole conjugates were designed and synthesized via multistep chemical reactions following the pharmacophoric hybridization approach. Compound 4h was identified as a promising MARK4 inhibitor with high selectivity toward MARK4 inhibition as compared to the panel of screened 30 kinases pertaining to the serine/threonine family, which was validated by molecular docking and fluorescence binding studies. The comprehensive cell-based examination divulged the promising apoptotic, antiproliferative, and antioxidant potential for the chemotype 4h. The compound 4h was endowed with the K _a value of 3.6 × 10³ M^-1 for human serum albumin, which reflects its remarkable transportation and delivery properties to the target site via blood. The present study impedes that in the future, such compounds may stand as optimized pharmacological lead candidates in drug discovery for targeting cancer via MARK4 inhibition with a remarkable anticancer profile.

Recent advances in phenotypic drug discovery

There is a great need for innovative new medicines to treat unmet medical needs. The discovery and development of innovative new medicines is extremely difficult, costly, and inefficient. In the last decade, phenotypic drug discovery (PDD) was reintroduced as a strategy to provide first-in-class medicines. PDD uses empirical, target-agnostic lead generation to identify pharmacologically active molecules and novel therapeutics which work through unprecedented drug mechanisms. The economic and scientific value of PDD is exemplified through game-changing medicines for hepatitis C virus, spinal muscular atrophy, and cystic fibrosis. In this short review, recent advances are noted for the implementation and de-risking of PDD (for compound library selection, biomarker development, mechanism identification, and safety studies) and the potential for artificial intelligence. A significant barrier in the decision to implement PDD is balancing the potential impact of a novel mechanism of drug action with an under-defined scientific path forward, with the desire to provide infrastructure and metrics to optimize return on investment, which a known mechanism provides. A means to address this knowledge gap in the future is to empower precompetitive research utilizing the empirical concepts of PDD to identify new mechanisms and pharmacologically active compounds.

Functional Molecular Network Analysis Enables Prediction of Response to Vedolizumab Therapy in Anti-TNF Refractory IBD Patients

Background

We applied for the first time 2 label-free technologies, physiological intermolecular modulation spectroscopy (PIMS) and nematic protein organization technic (NPOT) in anti-tumor necrosis factor (TNF) refractory inflammatory bowel disease (IBD) patients to identify clinical responders to vedolizumab therapy and elucidate their underlying functional molecular network.

Methods

PIMS analysis was performed in peripheral blood taken prior to the first vedolizumab application in 20 IBD patients (Crohn disease n = 13; ulcerative colitis n = 7) refractory to at least 1 previous anti-TNF agent therapy. Peripheral blood taken from clinical responders and nonresponders at week 14 of vedolizumab therapy were additionally subjected to NPOT analysis. Response to therapy was assessed by respective clinical disease activity scores (partial Mayo Score and Harvey-Bradshaw Index).

Results

Clinical response to vedolizumab treatment was observed in 7 of 13 Crohn disease and 4 of 7 ulcerative colitis patients at week 14. Response to therapy was accurately predicted by PIMS blood analysis in 100% of ulcerative colitis and 77% of Crohn disease patients. Overall prediction of clinical response with PIMS blood analysis was achieved with a 89% positive predictive value and a 82% negative predictive value. NPOT analysis revealed the heightened expression of the proteins ITGB7, ITGAV, ITG3, PF4, and ASGH in the peripheral blood of vedolizumab responders compared to nonresponders.

Conclusions

PIMS analysis of the blood of anti-TNF refractory IBD patients was able to stratify responders to vedolizumab therapy with high accuracy and specificity. NPOT technology could decipher underling molecular networks in the blood of responders, enabling subsequent personalized therapeutic approaches in IBD.

Target identification of natural medicine with chemical proteomics approach: probe synthesis, target fishing and protein identification

Natural products are an important source of new drugs for the treatment of various diseases. However, developing natural product-based new medicines through random moiety modification is a lengthy and costly process, due in part to the difficulties associated with comprehensively understanding the mechanism of action and the side effects. Identifying the protein targets of natural products is an effective strategy, but most medicines interact with multiple protein targets, which complicate this process. In recent years, an increasing number of researchers have begun to screen the target proteins of natural products with chemical proteomics approaches, which can provide a more comprehensive array of the protein targets of active small molecules in an unbiased manner. Typically, chemical proteomics experiments for target identification consist of two key steps: (1) chemical probe design and synthesis and (2) target fishing and identification. In recent decades, five different types of chemical proteomic probes and their respective target fishing methods have been developed to screen targets of molecules with different structures, and a variety of protein identification approaches have been invented. Presently, we will classify these chemical proteomics approaches, the application scopes and characteristics of the different types of chemical probes, the different protein identification methods, and the advantages and disadvantages of these strategies.

Mycobacterium tuberculosis Shikimate Pathway Enzymes as Targets for the Rational Design of Anti-Tuberculosis Drugs

Roughly a third of the world’s population is estimated to have latent Mycobacterium tuberculosis infection, being at risk of developing active tuberculosis (TB) during their lifetime. Given the inefficacy of prophylactic measures and the increase of drug-resistant M. tuberculosis strains, there is a clear and urgent need for the development of new and more efficient chemotherapeutic agents, with selective toxicity, to be implemented on patient treatment. The component enzymes of the shikimate pathway, which is essential in mycobacteria and absent in humans, stand as attractive and potential targets for the development of new drugs to treat TB. This review gives an update on published work on the enzymes of the shikimate pathway and some insight on what can be potentially explored towards selective drug development.

Chemical Proteomics and Phenotypic Profiling Identifies the Aryl Hydrocarbon Receptor as a Molecular Target of the Utrophin Modulator Ezutromid

Duchenne muscular dystrophy (DMD) is a fatal muscle-wasting disease arising from mutations in the dystrophin gene. Upregulation of utrophin to compensate for the missing dystrophin offers a potential therapy independent of patient genotype. The first-in-class utrophin modulator ezutromid/SMT C1100 was developed from a phenotypic screen through to a Phase 2 clinical trial. Promising efficacy and evidence of target engagement was observed in DMD patients after 24 weeks of treatment, however trial endpoints were not met after 48 weeks. The objective of this study was to understand the mechanism of action of ezutromid which could explain the lack of sustained efficacy and help development of new generations of utrophin modulators. Using chemical proteomics and phenotypic profiling we show that the aryl hydrocarbon receptor (AhR) is a target of ezutromid. Several lines of evidence demonstrate that ezutromid binds AhR with an apparent KD of 50 nm and behaves as an AhR antagonist. Furthermore, other reported AhR antagonists also upregulate utrophin, showing that this pathway, which is currently being explored in other clinical applications including oncology and rheumatoid arthritis, could also be exploited in future DMD therapies.

Identification of morpholine based hydroxylamine analogues: selective inhibitors of MARK4/Par-1d causing cancer cell death through apoptosis

Interaction of compound32with various active site residues of MARK4.

Drug Target Engagement Using Coupled Cellular Thermal Shift Assay-Acoustic Reverse-Phase Protein Array

In the last 5 years, cellular thermal shift assay (CETSA), a technology based on ligand-induced changes in protein thermal stability, has been increasingly used in drug discovery to address the fundamental question of whether drug candidates engage their intended target in a biologically relevant setting. To analyze lysates from cells submitted to increasing temperature, the detection and quantification of the remaining soluble protein can be achieved using quantitative mass spectrometry, Western blotting, or AlphaScreen techniques. Still, these approaches can be time- and cell-consuming. To cope with limitations of throughput and protein amount requirements, we developed a new coupled assay combining the advantages of a nanoacoustic transfer system and reverse-phase protein array technology within CETSA experiments. We validated the technology to assess engagement of inhibitors of insulin-degrading enzyme (IDE), an enzyme involved in diabetes and Alzheimer's disease. CETSA-acoustic reverse-phase protein array (CETSA-aRPPA) allows simultaneous analysis of many conditions and drug-target engagement with a small sample size, in a rapid, cost-effective, and biological material-saving manner.

New Advances in Cell Microarray Technology to Expand Applications in Target Deconvolution and Off-Target Screening

Identifying the cell surface receptors of natural ligands, and deconvoluting the receptor targets of candidate drug leads, presents a challenge in medical research and drug discovery. Traditionally, success rates have been low, and screening efforts often generate numerous false-positive hits that require extensive follow-up to either validate or disregard. If successful, receptor identification enables the discovery of previously unknown, disease-relevant targets, provides critical insights into biological pathways and disease processes, and allows for secondary targets to be uncovered. By expressing the majority of the human plasma membrane proteome in human cells on glass slides in situ, human cell microarray technology provides a powerful approach for identifying receptor target interactions. This approach significantly increases the success rates in identifying specific primary receptor targets and off-targets while limiting the number of false-positive hits. Here we describe cell microarray technology, focusing on new advances including the use of whole cells as bait for receptor interactions, and the inclusion of secreted proteins that widens the utility of the technology in off-target screening.

Cosolvent Analysis Toolkit (CAT): a robust hotspot identification platform for cosolvent simulations of proteins to expand the druggable proteome

Cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterization of allosteric and cryptic binding sites, which can be rendered “druggable” by small molecule ligands. Despite their conceptual simplicity and effectiveness, the analysis of cosolvent MD trajectories relies on pocket volume data, which requires a high level of manual investigation and may introduce a bias. In this work, we present CAT (Cosolvent Analysis Toolkit): an open-source, freely accessible analytical tool, suitable for automated analysis of cosolvent MD trajectories. CAT is compatible with commonly used molecular graphics software packages such as UCSF Chimera and VMD. Using a novel hybrid empirical force field scoring function, CAT accurately ranks the dynamic interactions between the macromolecular target and cosolvent molecules. To benchmark, CAT was used for three validated protein targets with allosteric and orthosteric binding sites, using five chemically distinct cosolvent molecules. For all systems, CAT has accurately identified all known sites. CAT can thus assist in computational studies aiming at identification of protein “hotspots” in a wide range of systems. As an easy-to-use computational tool, we expect that CAT will contribute to an increase in the size of the potentially ‘druggable’ human proteome.

Nonparametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-binding Proteins

Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present nonparametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP data sets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. An open source software implementation of NPARC is provided.

Identification of 2,6-Disubstituted 3H-Imidazo[4,5-b]pyridines as Therapeutic Agents for Dysferlinopathies through Phenotypic Screening on Patient-Derived Induced Pluripotent Stem Cells

Dysferlinopathies, which are muscular diseases caused by mutations in the dysferlin gene, remain serious medical problems due to the lack of therapeutic agents. Herein, we report the design, synthesis, and structure-activity relationships of a 2,6-disubstituted 3H-imidazo[4,5-b]pyridine series, which was identified from the phenotypic screening of chemicals that increase the level of dysferlin in myocytes differentiated from patient-derived induced pluripotent stem cells (iPSCs). Optimization studies with cell-based phenotypic assay led to the identification of a highly potent compound, 19, with dysferlin elevation effects at double-digit nanomolar concentrations. In addition, the molecular target of our chemical series was identified as tubulin, through a tubulin polymerization assay and a competitive binding assay using a photoaffinity labeling probe.

Identification of ebselen as a potent inhibitor of insulin degrading enzyme by a drug repurposing screening

Insulin-degrading enzyme, IDE, is a metalloprotease implicated in the metabolism of key peptides such as insulin, glucagon, β-amyloid peptide. Recent studies have pointed out its broader role in the cell physiology. In order to identify new drug-like inhibitors of IDE with optimal pharmacokinetic properties to probe its multiple roles, we ran a high-throughput drug repurposing screening. Ebselen, cefmetazole and rabeprazole were identified as reversible inhibitors of IDE. Ebselen is the most potent inhibitor (IC_50(insulin) = 14 nM). The molecular mode of action of ebselen was investigated by biophysical methods. We show that ebselen induces the disorder of the IDE catalytic cleft, which significantly differs from the previously reported IDE inhibitors. IDE inhibition by ebselen can explain some of its reported activities in metabolism as well as in neuroprotection.

Improving inverse docking target identification with Z-score selection

The utilization of inverse docking methods for target identification has been driven by an increasing demand for efficient tools for detecting potential drug side-effects. Despite impressive achievements in the field of inverse docking, identifying true positives from a pool of potential targets still remains challenging. Notably, most of the developed techniques have low accuracies, limit the pool of possible targets that can be investigated or are not easy to use for non-experts due to a lack of available scripts or webserver. Guided by our finding that the absolute docking score was a poor indication of a ligand's protein target, we developed a novel "combined Z-score" method that used a weighted fraction of ligand and receptor-based Z-scores to identify the most likely binding target of a ligand. With our combined Z-score method, an additional 14%, 3.6%, and 6.3% of all ligand-protein pairs of the Astex, DUD, and DUD-E databases, respectively, were correctly predicted compared to a docking score-based selection. The combined Z-score had the highest area under the curve in a ROC curve analysis of all three datasets and the enrichment factor for the top 1% predictions using the combined Z-score analysis was the highest for the Astex and DUD-E datasets. Additionally, we developed a user-friendly python script (compatible with both Python2 and Python3) that enables users to employ the combined Z-score analysis for target identification using a user-defined list of ligands and targets. We are providing this python script and a user tutorial as part of the supplemental information.