Evolution of commercially available compounds for HTS

High-Throughput Mining of Novel Compounds from Known Microbes: A Boost to Natural Product Screening

Advanced techniques can accelerate the pace of natural product discovery from microbes, which has been lagging behind the drug discovery era. Therefore, the present review article discusses the various interdisciplinary and cutting-edge techniques to present a concrete strategy that enables the high-throughput screening of novel natural compounds (NCs) from known microbes. Recent bioinformatics methods revealed that the microbial genome contains a huge untapped reservoir of silent biosynthetic gene clusters (BGC). This article describes several methods to identify the microbial strains with hidden mines of silent BGCs. Moreover, antiSMASH 5.0 is a free, accurate, and highly reliable bioinformatics tool discussed in detail to identify silent BGCs in the microbial genome. Further, the latest microbial culture technique, HiTES (high-throughput elicitor screening), has been detailed for the expression of silent BGCs using 500-1000 different growth conditions at a time. Following the expression of silent BGCs, the latest mass spectrometry methods are highlighted to identify the NCs. The recently emerged LAESI-IMS (laser ablation electrospray ionization-imaging mass spectrometry) technique, which enables the rapid identification of novel NCs directly from microtiter plates, is presented in detail. Finally, various trending 'dereplication' strategies are emphasized to increase the effectiveness of NC screening.

Outline and background for the EU-OS solubility prediction challenge

In June 2022, EU-OS came to the decision to make public a solubility data set of 100+K compounds obtained from several of the EU-OS proprietary screening compound collections. Leveraging on the interest of SLAS for screening scientific development it was decided to launch a joint EUOS-SLAS competition within the chemoinformatics and machine learning (ML) communities. The competition was open to real world computation experts, for the best, most predictive, classification model of compound solubility. The aim of the competition was multiple: from a practical side, the winning model should then serve as a cornerstone for future solubility predictions having used the largest training set so far publicly available. From a higher project perspective, the intent was to focus the energies and experiences, even if professionally not precisely coming from Pharma R&D; to address the issue of how to predict compound solubility. Here we report how the competition was ideated and the practical aspects of conducting it within the Kaggle framework, leveraging of the versatility and the open-source nature of this data science platform. Consideration on results and challenges encountered have been also examined.

Fragment-based drug discovery for disorders of the central nervous system: designing better drugs piece by piece

Fragment-based drug discovery (FBDD) has emerged as a powerful strategy to confront the challenges faced by conventional drug development approaches, particularly in the context of central nervous system (CNS) disorders. FBDD involves the screening of libraries that comprise thousands of small molecular fragments, each no greater than 300 Da in size. Unlike the generally larger molecules from high-throughput screening that limit customisation, fragments offer a more strategic starting point. These fragments are inherently compact, providing a strong foundation with good binding affinity for the development of drug candidates. The minimal elaboration required to transition the hit into a drug-like molecule is not only accelerated, but also it allows for precise modifications to enhance both their activity and pharmacokinetic properties. This shift towards a fragment-centric approach has seen commercial success and holds considerable promise in the continued streamlining of the drug discovery and development process. In this review, we highlight how FBDD can be integrated into the CNS drug discovery process to enhance the exploration of a target. Furthermore, we provide recent examples where FBDD has been an integral component in CNS drug discovery programs, enabling the improvement of pharmacokinetic properties that have previously proven challenging. The FBDD optimisation process provides a systematic approach to explore this vast chemical space, facilitating the discovery and design of compounds piece by piece that are capable of modulating crucial CNS targets.

Integrated workflow for the identification of new GABAA R positive allosteric modulators based on the in silico screening with further in vitro validation. Case study using Enamine's stock chemical space

Numerous studies reported an association between GABAA R subunit genes and epilepsy, eating disorders, autism spectrum disorders, neurodevelopmental disorders, and bipolar disorders. This study was aimed to find some potential positive allosteric modulators and was performed by combining the in silico approach with further in vitro evaluation of its real activity. We started from the GABAA R-diazepam complexes and assembled a lipid embedded protein ensemble to refine it via molecular dynamics (MD) simulation. Then we focused on the interaction of α1β2γ2 with some Z-drugs (non-benzodiazepine compounds) using an Induced Fit Docking (IFD) into the relaxed binding site to generate a pharmacophore model. The pharmacophore model was validated with a reference set and applied to decrease the pre-filtered Enamine database before the main docking procedure. Finally, we succeeded in identifying a set of compounds, which met all features of the docking model. The aqueous solubility and stability of these compounds in mouse plasma were assessed. Then they were tested for the biological activity using the rat Purkinje neurons and CHO cells with heterologously expressed human α1β2γ2 GABAA receptors. Whole-cell patch clamp recordings were used to reveal the GABA induced currents. Our study represents a convenient and tunable model for the discovery of novel positive allosteric modulators of GABAA receptors. A High-throughput virtual screening of the largest available database of chemical compounds resulted in the selection of 23 compounds. Further electrophysiological tests allowed us to determine a set of 3 the most outstanding active compounds. Considering the structural features of leader compounds, the study can develop into the MedChem project soon.

Target-based drug discovery: Applications of fluorescence techniques in high throughput and fragment-based screening

Target-based discovery of first-in-class therapeutics demands an in-depth understanding of the molecular mechanisms underlying human diseases. Precise measurements of cellular and biochemical activities are critical to gain mechanistic knowledge of biomolecules and their altered function in disease conditions. Such measurements enable the development of intervention strategies for preventing or treating diseases by modulation of desired molecular processes. Fluorescence-based techniques are routinely employed for accurate and robust measurements of in-vitro activity of molecular targets and for discovering novel chemical molecules that modulate the activity of molecular targets. In the current review, the authors focus on the applications of fluorescence-based high throughput screening (HTS) and fragment-based ligand discovery (FBLD) techniques such as fluorescence polarization (FP), Förster resonance energy transfer (FRET), fluorescence thermal shift assay (FTSA) and microscale thermophoresis (MST) for the discovery of chemical probe to exploring target's role in disease biology and ultimately, serve as a foundation for drug discovery. Some recent advancements in these techniques for compound library screening against important classes of drug targets, such as G-protein-coupled receptors (GPCRs) and GTPases, as well as phosphorylation- and acetylation-mediated protein-protein interactions, are discussed. Overall, this review presents a landscape of how these techniques paved the way for the discovery of small-molecule modulators and biologics against these targets for therapeutic benefits.

Discovery of tetrazolo-pyridazine-based small molecules as inhibitors of MACC1-driven cancer metastasis

Metastasis is directly linked to poor prognosis of cancer patients and warrants search for effective anti-metastatic drugs. MACC1 is a causal key molecule for metastasis. High MACC1 expression is prognostic for metastasis and poor survival. Here, we developed novel small molecule inhibitors targeting MACC1 expression to impede metastasis formation. We performed a human MACC1 promoter-driven luciferase reporter-based high-throughput screen (HTS; 118.500 compound library) to identify MACC1 transcriptional inhibitors. HTS revealed 1,2,3,4-tetrazolo[1,5-b]pyridazine-based compounds as efficient transcriptional inhibitors of MACC1 expression, able to decrease MACC1-induced cancer cell motility in vitro. Structure-activity relationships identified the essential inhibitory core structure. Best candidates were evaluated for metastasis inhibition in xenografted mouse models demonstrating metastasis restriction. ADMET showed high drug-likeness of these new candidates for cancer therapy. The NFκB pathway was identified as one mode of action targeted by these compounds. Taken together, 1,2,3,4-tetrazolo[1,5-b]pyridazine-based compounds are effective MACC1 inhibitors and pose promising candidates for anti-metastatic therapies particularly for patients with MACC1-overexpressing cancers, that are at high risk to develop metastases. Although further preclinical and clinical development is necessary, these compounds represent important building blocks for an individualized anti-metastatic therapy for solid cancers.

What drives researcher preferences for chemical compounds? Evidence from conjoint analysis

We investigated the attributes and attribute levels that affect researcher preferences for chemical compounds. We conducted a conjoint analysis on survey data of Korean researchers using chemical compounds from the Korean Chemical Bank (KCB). The analysis estimated the part-worth utility for each attribute's level, calculated relative importance of attributes, and classified user segmentation with different patterns. The results show that the structure database offers the highest part-worth utility to researchers, followed by high new functionality, price, screening service, and drug action data provided only by the KCB. Notably, researchers view the offer of a structured database and high new functionality as more important than other attributes in decision-making about research and development of chemical compounds. Furthermore, the results of segmentation analysis demonstrated that researchers have distinct usage patterns of chemical compounds: researchers consider structure database and high new functionality in cluster 1; and high new functionality and price in cluster 2, to be the most appealing. We discussed some policy and strategic implications based on the findings of this study and proposed some limitations.

Structural flexibility of apolipoprotein E-derived arginine-rich peptides improves their cell penetration capability

Amphipathic arginine-rich peptide, A2-17, exhibits moderate perturbation of lipid membranes and the highest cell penetration among its structural isomers. We investigated the direct cell-membrane penetration mechanism of the A2-17 peptide while focusing on structural flexibility. We designed conformationally constrained versions of A2-17, stapled (StpA2-17) and stitched (StchA2-17), whose α-helical conformations were stabilized by chemical crosslinking. Circular dichroism confirmed that StpA2-17 and StchA2-17 had higher α-helix content than A2-17 in aqueous solution. Upon liposome binding, only A2-17 exhibited a coil-to-helix transition. Confocal microscopy revealed that A2-17 had higher cell penetration efficiency than StpA2-17, whereas StchA2-17 remained on the cell membrane without cell penetration. Although the tryptophan fluorescence analysis suggested that A2-17 and its analogs had similar membrane-insertion positions between the interface and hydrophobic core, StchA2-17 exhibited a higher membrane affinity than A2-17 or StpA2-17. Atomic force microscopy demonstrated that A2-17 reduced the mechanical rigidity of liposomes to a greater extent than StpA2-17 and StchA2-17. Finally, electrophysiological analysis showed that A2-17 induced a higher charge influx through transient pores in a planer lipid bilayer than StpA2-17 and StchA2-17. These findings indicate that structural flexibility, which enables diverse conformations of A2-17, leads to a membrane perturbation mode that contributes to cell membrane penetration.

Biocatalysis in Drug Design: Engineered Reductive Aminases (RedAms) Are Used to Access Chiral Building Blocks with Multiple Stereocenters

Novel building blocks are in constant demand during the search for innovative bioactive small molecule therapeutics by enabling the construction of structure-activity-property-toxicology relationships. Complex chiral molecules containing multiple stereocenters are an important component in compound library expansion but can be difficult to access by traditional organic synthesis. Herein, we report a biocatalytic process to access a specific diastereomer of a chiral amine building block used in drug discovery. A reductive aminase (RedAm) was engineered following a structure-guided mutagenesis strategy to produce the desired isomer. The engineered RedAm (IR-09 W204R) was able to generate the (S,S,S)-isomer 3 in 45% conversion and 95% ee from the racemic ketone 2. Subsequent palladium-catalyzed deallylation of 3 yielded the target primary amine 4 in a 73% yield. This engineered biocatalyst was used at preparative scale and represents a potential starting point for further engineering and process development.

Relevance of the Trillion-Sized Chemical Space "eXplore" as a Source for Drug Discovery

Within the past two decades, virtual combinatorial compound collections, so-called chemical spaces, became an important molecule source for pharmaceutical research all over the world. The emergence of compound vendor chemical spaces with rapidly growing numbers of molecules raises questions about their application suitability and the quality of the content. Here, we examine the composition of the recently published and, so far, biggest chemical space, "eXplore", which comprises approximately 2.8 trillion virtual product molecules. The utility of eXplore to retrieve interesting chemistry around approved drugs and common Bemis Murcko scaffolds has been assessed with several methods (FTrees, SpaceLight, SpaceMACS). Further, the overlap between several vendor chemical spaces and a physicochemical property distribution analysis has been performed. Despite the straightforward chemical reactions underlying its setup, eXplore is demonstrated to provide relevant and, most importantly, easily accessible molecules for drug discovery campaigns.

Trifluoromethyl Vinamidinium Salt as a Promising Precursor for Fused β-Trifluoromethyl Pyridines

An efficient chlorotrimethylsilane-promoted synthetic protocol for the preparation of functionalized fused β-trifluoromethyl pyridines by cyclization of electron-rich aminoheterocycles or substituted anilines with a trifluoromethyl vinamidinium salt was developed. The efficient and scalable approach for producing represented trifluoromethyl vinamidinium salt demonstrated huge prospects for further use. The structure specificities of the trifluoromethyl vinamidinium salt and their impact on the reaction progress were determined. The procedure's scope and alternative ways of the reaction were investigated. The possibility of increasing the reaction scale up to 50 g and further modification of obtained products was shown. A minilibrary of potential fragments for ¹⁹F NMR-based fragment-based drug discovery (FBDD) was synthesized.

The Application of High-Throughput Approaches in Identifying Novel Therapeutic Targets and Agents to Treat Diabetes

During the past decades, unprecedented progress in technologies has revolutionized traditional research methodologies. Among these, advances in high-throughput drug screening approaches have permitted the rapid identification of potential therapeutic agents from drug libraries that contain thousands or millions of molecules. Moreover, high-throughput-based therapeutic target discovery strategies can comprehensively interrogate relationships between biomolecules (e.g., gene, RNA, and protein) and diseases and significantly increase the authors' knowledge of disease mechanisms. Diabetes is a chronic disease primarily characterized by the incapacity of the body to maintain normoglycemia. The prevalence of diabetes in modern society has become a severe public health issue that threatens the well-being of millions of patients. Although a number of pharmacological treatments are available, there is no permanent cure for diabetes, and discovering novel therapeutic targets and agents continues to be an urgent need. The present review discusses the technical details of high-throughput screening approaches in drug discovery, followed by introducing the applications of such approaches to diabetes research. This review aims to provide an example of the applicability of high-throughput technologies in facilitating different aspects of disease research.

Design and Synthesis of Fsp3-Enriched Spirocyclic-Based Biological Screening Compound Arrays via DOS Strategies and Their NNMT Inhibition Profiling

Medicinal chemists are keen to explore tridimensional compounds, especially when it comes to small molecules. It has already been stressed that the majority of known drugs tend to be flat, whereas natural products tend to be more tridimensional and represent a good source of active compounds. 3D metrics have been implemented and computational descriptors are available to evaluate and prioritize compounds based on their 3D geometry. This is usually done by comparing the saturated carbon atoms in a molecule with the total number of its non-hydrogen atoms (the Fsp3 value). While this aspect is clear, still there are not enough synthetic tools that support the realization of novel chemotypes that conform to these criteria. Herein we describe a diversity oriented synthesis (DOS) synthetic cascade technology that starts from two simple reagents, and generates highly enriched Fsp3 novel and diverse spiro-scaffolds with pragmatic synthetic handles (points of diversity). The spiro nature of these scaffolds not only ensures high Fsp3 values but renders the compounds more rigid and therefore more effective in forming precise stereo-interactions with their potential biological targets. These compounds were also profiled for their drug-like properties and as potential modulators of the NNMT enzyme.

The Time and Place for Nature in Drug Discovery

The case for a renewed focus on Nature in drug discovery is reviewed; not in terms of natural product screening, but how and why biomimetic molecules, especially those produced by natural processes, should deliver in the age of artificial intelligence and screening of vast collections both in vitro and in silico. The declining natural product-likeness of licensed drugs and the consequent physicochemical implications of this trend in the context of current practices are noted. To arrest these trends, the logic of seeking new bioactive agents with enhanced natural mimicry is considered; notably that molecules constructed by proteins (enzymes) are more likely to interact with other proteins (e.g., targets and transporters), a notion validated by natural products. Nature's finite number of building blocks and their interactions necessarily reduce potential numbers of structures, yet these enable expansion of chemical space with their inherent diversity of physical characteristics, pertinent to property-based design. The feasible variations on natural motifs are considered and expanded to encompass pseudo-natural products, leading to the further logical step of harnessing bioprocessing routes to access them. Together, these offer opportunities for enhancing natural mimicry, thereby bringing innovation to drug synthesis exploiting the characteristics of natural recognition processes. The potential for computational guidance to help identifying binding commonalities in the route map is a logical opportunity to enable the design of tailored molecules, with a focus on "organic/biological" rather than purely "synthetic" structures. The design and synthesis of prototype structures should pay dividends in the disposition and efficacy of the molecules, while inherently enabling greener and more sustainable manufacturing techniques.

High-throughput screening strategies for space-based radiation countermeasure discovery

As humanity begins to venture further into space, approaches to better protect astronauts from the hazards found in space need to be developed. One particular hazard of concern is the complex radiation that is ever present in deep space. Currently, it is unlikely enough spacecraft shielding could be launched that would provide adequate protection to astronauts during long-duration missions such as a journey to Mars and back. In an effort to identify other means of protection, prophylactic radioprotective drugs have been proposed as a potential means to reduce the biological damage caused by this radiation. Unfortunately, few radioprotectors have been approved by the FDA for usage and for those that have been developed, they protect normal cells/tissues from acute, high levels of radiation exposure such as that from oncology radiation treatments. To date, essentially no radioprotectors have been developed that specifically counteract the effects of chronic low-dose rate space radiation. This review highlights how high-throughput screening (HTS) methodologies could be implemented to identify such a radioprotective agent. Several potential target, pathway, and phenotypic assays are discussed along with potential challenges towards screening for radioprotectors. Utilizing HTS strategies such as the ones proposed here have the potential to identify new chemical scaffolds that can be developed into efficacious radioprotectors that are specifically designed to protect astronauts during deep space journeys. The overarching goal of this review is to elicit broader interest in applying drug discovery techniques, specifically HTS towards the identification of radiation countermeasures designed to be efficacious towards the biological insults likely to be encountered by astronauts on long duration voyages.

On drug discovery against infectious diseases and academic medicinal chemistry contributions

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a "good" hit in the course of a screening campaign still rely on a lot of luck - which is within the reach of academics -, that the hit to lead process requires a lot of chemistry and that if public-private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

The Ukrainian Factor in Early-Stage Drug Discovery in the Context of Russian Invasion: The Case of Enamine Ltd

Ukrainian companies occupy an important niche in the global drug discovery process; however, before the Russian invasion, the role of Ukraine was not obvious. The biggest Ukrainian fine chemical supplier, Enamine Ltd, had to stop operation for more than a month, which significantly affected various early-stage drug discovery projects. The role of Enamine in drug discovery and the company's past and future in the context of the Russian invasion are described in this Viewpoint.

Screening of Big Pharma’s Library against Various in-house Biological Targets

Open innovation initiatives provide opportunities for collaboration and sharing of knowledge and experience between industry, academia, and government institutions. Through open innovation, Merck is offering a Mini Library of 80 carefully selected compounds from previous research and development projects to a broader scientific community for testing in academic drug discovery projects. These compounds are predominantly drug-like and cover a broad range of molecular targets. They could potentially interact with other enzymes, receptors, transporters, and ion channels of interest. The Mini Library was tested on seven in-house enzymes (bacterial MurA, MurC ligase, and DdlB enzyme, human MAO-A/B, human BChE, and murine AChE), and several hits were identified. A follow-up series of structural analogues provided by Merck gave a more detailed insight into the accessibility and the quality of the hit compounds. For example, sartan derivatives were moderate inhibitors of MurC, whereas bisarylureas were potent, selective, nanomolar inhibitors of hMAO-B. Importantly, 3-n-butyl-substituted indoles were identified as low nanomolar selective inhibitors of hBChE. All in all, the hit derivatives provide new starting points for the further exploration of the chemical space of high-quality enzyme inhibitors.

Maximizing the integration of virtual and experimental screening in hit discovery

INTRODUCTION

Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approaches.

AREAS COVERED

After reviewing the recent trends in both experimental and virtual screening, the authors discuss different integration strategies from parallel, focused, sequential, and iterative screening. Strategic considerations are demonstrated in a number of real-life case studies.

EXPERT OPINION

Experimental and virtual screening are complementary approaches that should be integrated in lead discovery settings. Virtual screening can access extremely large synthetically feasible chemical space that can be effectively searched on GPU clusters or cloud architectures. Experimental screening provides reliable datasets by quantitative HTS applications, and DNA-encoded libraries (DEL) have enlarged the chemical space covered by these technologies. These developments, together with the use of artificial intelligence methods, represent new options for their efficient integration. The case studies discussed here demonstrate the benefits of complementary strategies, such as focused and iterative screening.

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

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

Promoting GAINs (Give Attention to Limitations in Assays) over PAINs Alerts: no PAINS, more GAINs

Many concepts and guidelines in medicinal chemistry have been introduced to aid in successful drug discovery and development. An example is the concept of Pan-Assay Interference Compounds (PAINS) and the elimination of such nuisance compounds from high-throughput screening (HTS) libraries. PAINs, along with other guidelines in medicinal chemistry, are like double-edged swords. If used appropriately, they may be beneficial for drug discovery and development. However, rigid and blind use of such concepts can hinder productivity. In this perspective, we introduce GAINS (give attention to limitations in assays) and highlight its relevance for successful drug discovery.

Nociceptin/orphanin FQ opioid receptor (NOP) selective ligand MCOPPB links anxiolytic and senolytic effects

Accumulation of senescent cells may drive age-associated alterations and pathologies. Senolytics are promising therapeutics that can preferentially eliminate senescent cells. Here, we performed a high-throughput automatized screening (HTS) of the commercial LOPAC®Pfizer library on aphidicolin-induced senescent human fibroblasts, to identify novel senolytics. We discovered the nociceptin receptor FQ opioid receptor (NOP) selective ligand 1-[1-(1-methylcyclooctyl)-4-piperidinyl]-2-[(3R)-3-piperidinyl]-1H-benzimidazole (MCOPPB, a compound previously studied as potential anxiolytic) as the best scoring hit. The ability of MCOPPB to eliminate senescent cells in in vitro models was further tested in mice and in C. elegans. MCOPPB reduced the senescence cell burden in peripheral tissues but not in the central nervous system. Mice and worms exposed to MCOPPB also exhibited locomotion and lipid storage changes. Mechanistically, MCOPPB treatment activated transcriptional networks involved in the immune responses to external stressors, implicating Toll-like receptors (TLRs). Our study uncovers MCOPPB as a NOP ligand that, apart from anxiolytic effects, also shows tissue-specific senolytic effects.

Discovery and Development of the Quininib Series of Ocular Drugs

The quininib series is a novel collection of small-molecule drugs with antiangiogenic, antivascular permeability, anti-inflammatory, and antiproliferative activity. Quininib was initially identified as a drug hit during a random chemical library screen for determinants of developmental ocular angiogenesis in zebrafish. To enhance drug efficacy, novel quininib analogs were designed by applying medicinal chemistry approaches. The resulting quininib drug series has efficacy in in vitro and ex vivo models of angiogenesis utilizing human cell lines and tissues. In vivo, quininib drugs reduce pathological angiogenesis and retinal vascular permeability in rodent models. Quininib acts as a cysteinyl leukotriene (CysLT) receptor antagonist, revealing new roles of these G-protein-coupled receptors in developmental angiogenesis of the eye and unexpectedly in uveal melanoma (UM). The quininib series highlighted the potential of CysLT receptors as therapeutic targets for retinal vasculopathies (e.g., neovascular age-related macular degeneration, diabetic retinopathy, and diabetic macular edema) and ocular cancers (e.g., UM).

Discovery of compounds with viscosity-reducing effects on biopharmaceutical formulations with monoclonal antibodies

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Computational screening yielded 44 new viscosity-reducing agents on two model mAbs.

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Dual excipients for viscosity reduction and solution buffering were discovered.

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Compounds with three or more charges reduce the viscosity of model mAb formulations.

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Filtering based on physicochemical properties can be applied to other mAb formulations.

For the development of concentrated monoclonal antibody formulations for subcutaneous administration, the main challenge is the high viscosity of the solutions. To compensate for this, viscosity reducing agents are commonly used as excipients. Here, we applied two computational chemistry approaches to discover new viscosity-reducing agents: fingerprint similarity searching, and physicochemical property filtering. In total, 94 compounds were selected and experimentally evaluated on two model monoclonal antibodies, which led to the discovery of 44 new viscosity-reducing agents. Analysis of the results showed that using a simple filter that selects only compounds with three or more charge groups is a good ‘rule of thumb’ for selecting potential viscosity-reducing agents for two model monoclonal antibody formulations.

The costs and benefits of senotherapeutics for human health

Cellular senescence is a major contributor to age-related diseases in humans; however, it also has a beneficial role in physiological and pathological processes, including wound healing, host immunity, and tumour suppression. Reducing the burden of cell senescence in animal models of cardiometabolic disorders, inflammatory conditions, neurodegenerative diseases, and cancer using pharmaceutical approaches that selectively target senescent cells (ie, senolytics) or that suppress senescence-associated secretory phenotype (ie, senomorphics) holds great promise for the management of chronic age-associated conditions. Although studies have provided evidence that senolytics or senomorphics are effective at decreasing the number of senescent cells in humans, the short-term and long-term side-effects of these therapies are largely unknown. In this Review, we systematically discuss the senolytics and senomorphics that have been investigated in clinical trials or have been used off-label, presenting their various adverse effects. Despite the potential of senotherapeutics to transform anti-ageing medicine, a cautionary approach regarding unwanted dose-dependent side-effects should be adopted.

A Close-up Look at the Chemical Space of Commercially Available Building Blocks for Medicinal Chemistry

The ability to efficiently synthesize desired compounds can be a limiting factor for chemical space exploration in drug discovery. This ability is conditioned not only by the existence of well-studied synthetic protocols but also by the availability of corresponding reagents, so-called building blocks (BBs). In this work, we present a detailed analysis of the chemical space of 400 000 purchasable BBs. The chemical space was defined by corresponding synthons─fragments contributed to the final molecules upon reaction. They allow an analysis of BB physicochemical properties and diversity, unbiased by the leaving and protective groups in actual reagents. The main classes of BBs were analyzed in terms of their availability, rule-of-two-defined quality, and diversity. Available BBs were eventually compared to a reference set of biologically relevant synthons derived from ChEMBL fragmentation, in order to illustrate how well they cover the actual medicinal chemistry needs. This was performed on a newly constructed universal generative topographic map of synthon chemical space that enables visualization of both libraries and analysis of their overlapped and library-specific regions.

Virtual Screening in Search for a Chemical Probe for Angiotensin-Converting Enzyme 2 (ACE2)

We elaborate new models for ACE and ACE2 receptors with an excellent prediction power compared to previous models. We propose promising workflows for working with huge compound collections, thereby enabling us to discover optimized protocols for virtual screening management. The efficacy of elaborated roadmaps is demonstrated through the cost-effective molecular docking of 1.4 billion compounds. Savings of up to 10-fold in CPU time are demonstrated. These developments allowed us to evaluate ACE2/ACE selectivity in silico, which is a crucial checkpoint for developing chemical probes for ACE2.

Frontiers in antibiotic alternatives for Clostridioides difficile infection

Clostridioides difficile (C. difficile) is a gram-positive, anaerobic spore-forming bacterium and a major cause of antibiotic-associated diarrhea. Humans are naturally resistant to C. difficile infection (CDI) owing to the protection provided by healthy gut microbiota. When the gut microbiota is disturbed, C. difficile can colonize, produce toxins, and manifest clinical symptoms, ranging from asymptomatic diarrhea and colitis to death. Despite the steady-if not rising-prevalence of CDI, it will certainly become more problematic in a world of antibiotic overuse and the post-antibiotic era. C. difficile is naturally resistant to most of the currently used antibiotics as it uses multiple resistance mechanisms. Therefore, current CDI treatment regimens are extremely limited to only a few antibiotics, which include vancomycin, fidaxomicin, and metronidazole. Therefore, one of the main challenges experienced by the scientific community is the development of alternative approaches to control and treat CDI. In this Frontier article, we collectively summarize recent advances in alternative treatment approaches for CDI. Over the past few years, several studies have reported on natural product-derived compounds, drug repurposing, high-throughput library screening, phage therapy, and fecal microbiota transplantation. We also include an update on vaccine development, pre- and pro-biotics for CDI, and toxin antidote approaches. These measures tackle CDI at every stage of disease pathology via multiple mechanisms. We also discuss the gaps and concerns in these developments. The next epidemic of CDI is not a matter of if but a matter of when. Therefore, being well-equipped with a collection of alternative therapeutics is necessary and should be prioritized.

Photochemical synthesis of an epigenetic focused tetrahydroquinoline library

Discovery of epigenetic chemical probes is an important area of research with potential to deliver drugs for a multitude of diseases. However, commercially available libraries frequently used in drug discovery campaigns contain molecules that are focused on a narrow range of chemical space primarily driven by ease of synthesis and previously targeted enzyme classes (e.g., kinases) resulting in low hit rates for epigenetic targets. Here we describe the design and synthesis of a compound collection that augments current screening collections by the inclusion of privileged isosteres for epigenetic targets.

High throughput screening identifies inhibitors for parvovirus B19 infection of human erythroid progenitor cells

Parvovirus B19 (B19V) infection can cause hematological disorders and fetal hydrops during pregnancy. Currently, no antivirals or vaccines are available for the treatment or the prevention of B19V infection. To identify novel small-molecule antivirals against B19V replication, we developed a high throughput screening assay, which is based on an in vitro nicking assay using recombinant N-terminal 1-176 amino acids of the viral large nonstructural protein (NS1N) and a fluorescently labeled DNA probe (OriQ) that spans the nicking site of the viral DNA replication origin. We collectively screened 17,040 compounds and identified 2,178 (12.78%) hits that possess >10% inhibition of the NS1 nicking activity, among which 84 hits were confirmed to inhibit nicking in a dose-dependent manner. Using ex vivo expanded primary human erythroid progenitor cells (EPCs) infected by B19V, we validated 24 compounds demonstrated >50% in vivo inhibition of B19V infection at 10 μM, which can be categorized into 7 structure scaffolds. Based on the therapeutic index [half maximal cytotoxic concentration (CC₅₀)/half maximal effective concentration (EC₅₀)] in EPCs, the top 4 compounds were chosen to examine their inhibitions of B19V infection in EPCs at two times of the 90% maximal effective concentration (EC₉₀). A purine derivative (P7), demonstrated an antiviral effect (EC₅₀=1.46 μM) without prominent cytotoxicity (CC₅₀=71.8 μM) in EPCs, exhibited 92% inhibition of B19V infection in EPCs at 3.32 μM, which can be used as the lead compound in future studies for the treatment of B19V infection caused hematological disorders. Importance B19V encodes a large non-structural protein NS1. Its N-terminal domain (NS1N) consisting of 1-176 amino acids binds to viral DNA and serves as an endonuclease to nick the viral DNA replication origins, which is a pivotal step in rolling hairpin-dependent B19V DNA replication. For high throughput screening (HTS) of anti-B19V antivirals, we miniaturized a fluorescence-based in vitro nicking assay, which employs a fluorophore-labeled probe spanning the trs and the NS1N protein, into a 384-well plate format. The HTS assay showed a high reliability and capability in screening 17,040 compounds. Based on the therapeutic index [half maximal cytotoxic concentration (CC₅₀)/half maximal effective concentration (EC₅₀)] in EPCs, a purine derivative demonstrated an antiviral effect of 92% inhibition of B19V infection in EPCs at 3.32 μM (two times EC₉₀). Our study demonstrated a robust HTS assay for screening antivirals against B19V infection.

DNA-Encoded Chemistry: Drug Discovery from a Few Good Reactions

Click chemistry, proposed nearly 20 years ago, promised access to novel chemical space by empowering combinatorial library synthesis with a "few good reactions". These click reactions fulfilled key criteria (broad scope, quantitative yield, abundant starting material, mild reaction conditions, and high chemoselectivity), keeping the focus on molecules that would be easy to make, yet structurally diverse. This philosophy bears a striking resemblance to DNA-encoded library (DEL) technology, the now-dominant combinatorial chemistry paradigm. This review highlights the similarities between click and DEL reaction design and deployment in combinatorial library settings, providing a framework for the design of new DEL synthesis technologies to enable next-generation drug discovery.

Cu-Catalyzed Pyridine Synthesis via Oxidative Annulation of Cyclic Ketones with Propargylamine

A Cu-catalyzed, easily scalable one-pot synthesis of fused pyridines by the reaction of cyclic ketones with propargylamine is described. The protocol was optimized based on the results of more than 30 experiments. The highest product yields were achieved in i-PrOH as a solvent in the presence of 5.0 mol % CuCl₂ in air. In contrast to the well-known Au-catalyzed protocol, our procedure is "laboratory friendly", cost-effective, and suitable for preparing dozens of grams of fused pyridine-based building blocks and does not require a high-pressure autoclave technique. Decreasing the catalyst amount in the reaction to 1.25 mol % CuCl₂ provided a yield comparable to that achieved with 5 mol % catalyst, though a longer reaction time was required. A plausible reaction mechanism was proposed. The scope and limitation of the reaction were studied using 24 different cyclic ketones as starting materials. The fused pyridine yield decreased among cyclic ketones in the following order: six-membered ≫ eight-membered > five-membered ∼ seven-membered. The elaborated reaction conditions demonstrated tolerance to a number of protective functional groups in ketone such as ester, tert-butoxycarbonyl (Boc)-protected amine, and acetal moieties.

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.

Chemography: Searching for Hidden Treasures

The days when medicinal chemistry was limited to a few series of compounds of therapeutic interest are long gone. Nowadays, no human may succeed to acquire a complete overview of more than a billion existing or feasible compounds within which the potential "blockbuster drugs" are well hidden and yet only a few mouse clicks away. To reach these "hidden treasures", we adapted the generative topographic mapping method to enable efficient navigation through the chemical space, from a global overview to a structural pattern detection, covering, for the first time, the complete ZINC library of purchasable compounds, relative to 1.6 million biologically relevant ChEMBL molecules. About 40 000 hierarchical maps of the chemical space were constructed. Structural motifs inherent to only one library were identified. Roughly 20 000 off-market ChEMBL compound families represent incentives to enrich commercial catalogs. Alternatively, 125 000 ZINC-specific compound classes, absent in structure-activity bases, are novel paths to explore in medicinal chemistry. The complete list of these chemotypes can be downloaded using the link https://forms.gle/B6bUJj82t9EfmttV6.

Parallel Generative Topographic Mapping: An Efficient Approach for Big Data Handling

Generative Topographic Mapping (GTM) can be efficiently used to visualize, analyze and model large chemical data. The GTM manifold needs to span the chemical space deemed relevant for a given problem. Therefore, the Frame set (FS) of compounds used for the manifold construction must well cover a given chemical space. Intuitively, the FS size must raise with the size and diversity of the target library. At the same time, the GTM training can be very slow or even becomes technically impossible at FS sizes of the order of 105 compounds - which is a very small number compared to today's commercially accessible compounds, and, especially, to the theoretically feasible molecules. In order to solve this problem, we propose a Parallel GTM algorithm based on the merging of "intermediate" manifolds constructed in parallel for different subsets of molecules. An ensemble of these subsets forms a FS for the "final" manifold. In order to assess the efficiency of the new algorithm, 80 GTMs were built on the FSs of different sizes ranging from 10 to 1.8 M compounds selected from the ChEMBL database. Each GTM was challenged to build classification models for up to 712 biological activities (depending on the FS size). With the novel parallel GTM procedure, we could thus cover the entire spectrum of possible FS sizes, whereas previous studies were forced to rely on the working hypothesis that FS sizes of few thousands of compounds are sufficient to describe the ChEMBL chemical space. In fact, this study formally proves this to be true: a FS containing only 5000 randomly picked compounds is sufficient to represent the entire ChEMBL collection (1.8 M molecules), in the sense that a further increase of FS compound numbers has no benefice impact on the predictive propensity of the above-mentioned 712 activity classification models. Parallel GTM may, however, be required to generate maps based on very large FS, that might improve chemical space cartography of big commercial and virtual libraries, approaching billions of compounds.

Statistical models for identifying frequent hitters in high throughput screening

High throughput screening (HTS) interrogates compound libraries to find those that are “active” in an assay. To better understand compound behavior in HTS, we assessed an existing binomial survivor function (BSF) model of “frequent hitters” using 872 publicly available HTS data sets. We found large numbers of “infrequent hitters” using this model leading us to reject the BSF for identifying “frequent hitters.” As alternatives, we investigated generalized logistic, gamma, and negative binomial distributions as models for compound behavior. The gamma model reduced the proportion of both frequent and infrequent hitters relative to the BSF. Within this data set, conclusions about individual compound behavior were limited by the number of times individual compounds were tested (1–1613 times) and disproportionate testing of some compounds. Specifically, most tests (78%) were on a 309,847-compound subset (17.6% of compounds) each tested ≥ 300 times. We concluded that the disproportionate retesting of some compounds represents compound repurposing at scale rather than drug discovery. The approach to drug discovery represented by these 872 data sets characterizes the assays well by challenging them with many compounds while each compound is characterized poorly with a single assay. Aggregating the testing information from each compound across the multiple screens yielded a continuum with no clear boundary between normal and frequent hitting compounds.

Chemical Screening of Nuclear Receptor Modulators

Nuclear receptors are ligand-inducible transcriptional factors that control multiple biological phenomena, including proliferation, differentiation, reproduction, metabolism, and the maintenance of homeostasis. Members of the nuclear receptor superfamily have marked structural and functional similarities, and their domain functionalities and regulatory mechanisms have been well studied. Various modulators of nuclear receptors, including agonists and antagonists, have been developed as tools for elucidating nuclear receptor functions and also as drug candidates or lead compounds. Many assay systems are currently available to evaluate the modulation of nuclear receptor functions, and are useful as screening tools in the discovery and development of new modulators. In this review, we cover the chemical screening methods for nuclear receptor modulators, focusing on assay methods and chemical libraries for screening. We include some recent examples of the discovery of nuclear receptor modulators.

Generative topographic mapping in drug design

This is a review article of Generative Topographic Mapping (GTM) - a non-linear dimensionality reduction technique producing generative 2D maps of high-dimensional vector spaces - and its specific applications in Drug Design (chemical space cartography, compound library design and analysis, virtual screening, pharmacological profiling, de novo drug design, conformational space & docking interaction cartography, etc.) Written by chemoinformaticians for potential users among medicinal chemists and biologists, the article purposely avoids all underlying mathematics. First, the GTM concept is intuitively explained, based on the strong analogies with the rather popular Self-Organizing Maps (SOMs), which are well established library analysis tools. GTM is basically a fuzzy-logics-based generalization of SOMs. The second part of the review, some of published GTM applications in drug design are briefly revisited.

The Symbiotic Relationship Between Drug Discovery and Organic Chemistry

All pharmaceutical products contain organic molecules; the source may be a natural product or a fully synthetic molecule, or a combination of both. Thus, it follows that organic chemistry underpins both existing and upcoming pharmaceutical products. The reverse relationship has also affected organic synthesis, changing its landscape towards increasingly complex targets. This Review article sets out to give a concise appraisal of this symbiotic relationship between organic chemistry and drug discovery, along with a discussion of the design concepts and highlighting key milestones along the journey. In particular, criteria for a high-quality compound library design enabling efficient virtual navigation of chemical space, as well as rise and fall of concepts for its synthetic exploration (such as combinatorial chemistry; diversity-, biology-, lead-, or fragment-oriented syntheses; and DNA-encoded libraries) are critically surveyed.

Reaching for the bright StARs in chemical space

Visualization of activity data in chemical space is common in drug discovery. Navigating the space in a systematic manner is not trivial, given its size and huge coverage. To this end, methods for data visualization have been developed charting biological activity into chemical space. Herein, we review the progress in different visualization approaches to explore the chemical space aiming at reaching insightful structure-activity relationships (SARs) in the chemical space. We discuss recent methods including consensus diversity plots, ChemMaps, and constellation plots. Several of the methods we review can be extended to analyze other properties of interest in medicinal chemistry, such as structure-toxicity relationships, and can be adapted to postprocess results of virtual screening (VS) of large compound libraries.