Large-scale chemical-genetics yields new M. tuberculosis inhibitor classes.

New antibiotics are needed to combat rising levels of resistance, with new Mycobacterium tuberculosis (Mtb) drugs having the highest priority. However, conventional whole-cell and biochemical antibiotic screens have failed. Here we develop a strategy termed PROSPECT (primary screening of strains to prioritize expanded chemistry and targets), in which we screen compounds against pools of strains depleted of essential bacterial targets. We engineered strains that target 474 essential Mtb genes and screened pools of 100-150 strains against activity-enriched and unbiased compound libraries, probing more than 8.5 million chemical-genetic interactions. Primary screens identified over tenfold more hits than screening wild-type Mtb alone, with chemical-genetic interactions providing immediate, direct target insights. We identified over 40 compounds that target DNA gyrase, the cell wall, tryptophan, folate biosynthesis and RNA polymerase, as well as inhibitors that target EfpA. Chemical optimization yielded EfpA inhibitors with potent wild-type activity, thus demonstrating the ability of PROSPECT to yield inhibitors against targets that would have eluded conventional drug discovery.

SwissBioisostere 2021: updated structural, bioactivity and physicochemical data delivered by a reshaped web interface.

At several stages of drug discovery, bioisosteric replacement is a common and efficient practice to find new bioactive chemotypes or to optimize series of molecules toward drug candidates. The critical steps consisting in selecting which molecular moiety should be replaced by which other chemical fragment is often relying on the expertise of specialists. Nowadays, valuable support can be obtained through the wealth of dedicated structural and knowledge data. The present article details the update of SwissBioisostere, a database of >25 millions of unique molecular replacements with data on bioactivity, physicochemistry, chemical and biological contexts extracted from the literature and related resources. The content of the database together with analysis and visualization capacities is freely available at www.swissbioisostere.ch.

PROTAC-DB: an online database of PROTACs.

Proteolysis-targeting chimeras (PROTACs), which selectively degrade targeted proteins by the ubiquitin-proteasome system, have emerged as a novel therapeutic technology with potential advantages over traditional inhibition strategies. In the past few years, this technology has achieved substantial progress and two PROTACs have been advanced into phase I clinical trials. However, this technology is still maturing and the design of PROTACs remains a great challenge. In order to promote the rational design of PROTACs, we present PROTAC-DB, a web-based open-access database that integrates structural information and experimental data of PROTACs. Currently, PROTAC-DB consists of 1662 PROTACs, 202 warheads (small molecules that target the proteins of interest), 65 E3 ligands (small molecules capable of recruiting E3 ligases) and 806 linkers, as well as their chemical structures, biological activities, and physicochemical properties. Except the biological activities of warheads and E3 ligands, PROTAC-DB also provides the degradation capacities, binding affinities and cellular activities for PROTACs. PROTAC-DB can be queried with two general searching approaches: text-based (target name, compound name or ID) and structure-based. In addition, for the convenience of users, a filtering tool for the searching results based on the physicochemical properties of compounds is also offered. PROTAC-DB is freely accessible at http://cadd.zju.edu.cn/protacdb/.

Global Substance Registration System: consistent scientific descriptions for substances related to health.

The US Food and Drug Administration (FDA) and the National Center for Advancing Translational Sciences (NCATS) have collaborated to publish rigorous scientific descriptions of substances relevant to regulated products. The FDA has adopted the global ISO 11238 data standard for the identification of substances in medicinal products and has populated a database to organize the agency's regulatory submissions and marketed products data. NCATS has worked with FDA to develop the Global Substance Registration System (GSRS) and produce a non-proprietary version of the database for public benefit. In 2019, more than half of all new drugs in clinical development were proteins, nucleic acid therapeutics, polymer products, structurally diverse natural products or cellular therapies. While multiple databases of small molecule chemical structures are available, this resource is unique in its application of regulatory standards for the identification of medicinal substances and its robust support for other substances in addition to small molecules. This public, manually curated dataset provides unique ingredient identifiers (UNIIs) and detailed descriptions for over 100 000 substances that are particularly relevant to medicine and translational research. The dataset can be accessed and queried at https://gsrs.ncats.nih.gov/app/substances.

Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP.

The bile salt export pump (BSEP) actively transports conjugated monovalent bile acids from the hepatocytes into the bile. This facilitates the formation of micelles and promotes digestion and absorption of dietary fat. Inhibition of BSEP leads to decreased bile flow and accumulation of cytotoxic bile salts in the liver. A number of compounds have been identified to interact with BSEP, which results in drug-induced cholestasis or liver injury. Therefore, in silico approaches for flagging compounds as potential BSEP inhibitors would be of high value in the early stage of the drug discovery pipeline. Up to now, due to the lack of a high-resolution X-ray structure of BSEP, in silico based identification of BSEP inhibitors focused on ligand-based approaches. In this study, we provide a homology model for BSEP, developed using the corrected mouse P-glycoprotein structure (PDB ID: 4M1M). Subsequently, the model was used for docking-based classification of a set of 1212 compounds (405 BSEP inhibitors, 807 non-inhibitors). Using the scoring function ChemScore, a prediction accuracy of 81% on the training set and 73% on two external test sets could be obtained. In addition, the applicability domain of the models was assessed based on Euclidean distance. Further, analysis of the protein-ligand interaction fingerprints revealed certain functional group-amino acid residue interactions that could play a key role for ligand binding. Though ligand-based models, due to their high speed and accuracy, remain the method of choice for classification of BSEP inhibitors, structure-assisted docking models demonstrate reasonably good prediction accuracies while additionally providing information about putative protein-ligand interactions.

Clusters of bioactive compounds target dynamic endomembrane networks in vivo.

Endomembrane trafficking relies on the coordination of a highly complex, dynamic network of intracellular vesicles. Understanding the network will require a dissection of cargo and vesicle dynamics at the cellular level in vivo. This is also a key to establishing a link between vesicular networks and their functional roles in development. We used a high-content intracellular screen to discover small molecules targeting endomembrane trafficking in vivo in a complex eukaryote, Arabidopsis thaliana. Tens of thousands of molecules were prescreened and a selected subset was interrogated against a panel of plasma membrane (PM) and other endomembrane compartment markers to identify molecules that altered vesicle trafficking. The extensive image dataset was transformed by a flexible algorithm into a marker-by-phenotype-by-treatment time matrix and revealed groups of molecules that induced similar subcellular fingerprints (clusters). This matrix provides a platform for a systems view of trafficking. Molecules from distinct clusters presented avenues and enabled an entry point to dissect recycling at the PM, vacuolar sorting, and cell-plate maturation. Bioactivity in human cells indicated the value of the approach to identifying small molecules that are active in diverse organisms for biology and drug discovery.

Quantitative structure-activity relationship models for ready biodegradability of chemicals.

The European REACH regulation requires information on ready biodegradation, which is a screening test to assess the biodegradability of chemicals. At the same time REACH encourages the use of alternatives to animal testing which includes predictions from quantitative structure-activity relationship (QSAR) models. The aim of this study was to build QSAR models to predict ready biodegradation of chemicals by using different modeling methods and types of molecular descriptors. Particular attention was given to data screening and validation procedures in order to build predictive models. Experimental values of 1055 chemicals were collected from the webpage of the National Institute of Technology and Evaluation of Japan (NITE): 837 and 218 molecules were used for calibration and testing purposes, respectively. In addition, models were further evaluated using an external validation set consisting of 670 molecules. Classification models were produced in order to discriminate biodegradable and nonbiodegradable chemicals by means of different mathematical methods: k nearest neighbors, partial least squares discriminant analysis, and support vector machines, as well as their consensus models. The proposed models and the derived consensus analysis demonstrated good classification performances with respect to already published QSAR models on biodegradation. Relationships between the molecular descriptors selected in each QSAR model and biodegradability were evaluated.

Deep Annotation of Donated Chemical Probes (DCP) in Organotypic Human Liver Cultures and Patient-Derived Organoids from Tumor and Normal Colorectum.

Well-characterized small molecules are essential tools for studying the biology and therapeutic relevance of a target protein. However, many compounds reported in the literature and routinely studied in biomedical research lack the potency and selectivity required for mechanistic cellular studies on the function of a given protein. Furthermore, commercially available compounds often do not include useful tools developed by industry as part of their research and development efforts, as they frequently remain proprietary. The freely available donated chemical probe (DCP) library, fueled by generous donations of compounds from industry and academia, enables easy access to a steadily growing collection of these valuable and well-characterized tools. Here, we provide a systematic description of the current DCP library collection and their associated comprehensive characterization data, including a variety of in vitro and cellular assays. Of note, we characterized the set in relevant human primary models by employing hepatotoxicity screening in primary human liver spheroids and viability screening in patient-derived colorectal cancer organoids and matched normal-adjacent epithelium. Taken together, the DCP library represents a well-annotated, openly available collection of tool compounds for studying a wide range of targets, including kinases, G-protein-coupled receptors, and ion channels. As such, it represents a unique resource for the biomedical research community.

Speeding up chemical searches using the inverted index: the convergence of chemoinformatics and text search methods.

In ligand-based screening, retrosynthesis, and other chemoinformatics applications, one often seeks to search large databases of molecules in order to retrieve molecules that are similar to a given query. With the expanding size of molecular databases, the efficiency and scalability of data structures and algorithms for chemical searches are becoming increasingly important. Remarkably, both the chemoinformatics and information retrieval communities have converged on similar solutions whereby molecules or documents are represented by binary vectors, or fingerprints, indexing their substructures such as labeled paths for molecules and n-grams for text, with the same Jaccard-Tanimoto similarity measure. As a result, similarity search methods from one field can be adapted to the other. Here we adapt recent, state-of-the-art, inverted index methods from information retrieval to speed up similarity searches in chemoinformatics. Our results show a several-fold speed-up improvement over previous methods for both threshold searches and top-K searches. We also provide a mathematical analysis that allows one to predict the level of pruning achieved by the inverted index approach and validate the quality of these predictions through simulation experiments. All results can be replicated using data freely downloadable from http://cdb.ics.uci.edu/ .

Biosensor-based small molecule fragment screening with biolayer interferometry.

Biosensor-based fragment screening is a valuable tool in the drug discovery process. This method is advantageous over many biochemical methods because primary hits can be distinguished from non-specific or non-ideal interactions by examining binding profiles and responses, resulting in reduced false-positive rates. Biolayer interferometry (BLI), a technique that measures changes in an interference pattern generated from visible light reflected from an optical layer and a biolayer containing proteins of interest, is a relatively new method for monitoring small molecule interactions. The BLI format is based on a disposable sensor that is immersed in 96-well or 384-well plates. BLI has been validated for small molecule detection and fragment screening with model systems and well-characterized targets where affinity constants and binding profiles are generally similar to those obtained with surface plasmon resonsance (SPR). Screens with challenging targets involved in protein-protein interactions including BCL-2, JNK1, and eIF4E were performed with a fragment library of 6,500 compounds, and hit rates were compared for these targets. For eIF4E, a protein containing a PPI site and a nucleotide binding site, results from a BLI fragment screen were compared to results obtained in biochemical HTS screens. Overlapping hits were observed for the PPI site, and hits unique to the BLI screen were identified. Hit assessments with SPR and BLI are described.

Using Proteolytic Hypomorphs to Detect Small Molecule Mechanism of Action.

With increasing prevalence of antimicrobial resistance, a fundamental goal of antibiotic discovery is to uncover new small molecules that prevent growth of pathogenic bacteria through diverse mechanisms of action. This goal is particularly pertinent for tuberculosis, caused by Mycobacterium tuberculosis. In this chapter, we describe the application of a chemical-genetic method, PROSPECT (primary screening of strains to prioritize expanded chemistry and targets), for sensitively detecting small molecule bioactivity using a pooled panel of hypomorphs (strains depleted in a particular essential gene) of M. tuberculosis. We describe statistical and heuristic approaches to assign small molecule mechanism of action from the resulting chemical-genetic interaction profiles.

Metabolic drug survey highlights cancer cell dependencies and vulnerabilities.

Interrogation of cellular metabolism with high-throughput screening approaches can unravel contextual biology and identify cancer-specific metabolic vulnerabilities. To systematically study the consequences of distinct metabolic perturbations, we assemble a comprehensive metabolic drug library (CeMM Library of Metabolic Drugs; CLIMET) covering 243 compounds. We, next, characterize it phenotypically in a diverse panel of myeloid leukemia cell lines and primary patient cells. Analysis of the drug response profiles reveals that 77 drugs affect cell viability, with the top effective compounds targeting nucleic acid synthesis, oxidative stress, and the PI3K/mTOR pathway. Clustering of individual drug response profiles stratifies the cell lines into five functional groups, which link to specific molecular and metabolic features. Mechanistic characterization of selective responses to the PI3K inhibitor pictilisib, the fatty acid synthase inhibitor GSK2194069, and the SLC16A1 inhibitor AZD3965, bring forth biomarkers of drug response. Phenotypic screening using CLIMET represents a valuable tool to probe cellular metabolism and identify metabolic dependencies at large.

Small-molecule modulators of INAVA cytosolic condensate and cell-cell junction assemblies.

Epithelial cells lining mucosal surfaces distinctively express the inflammatory bowel disease risk gene INAVA. We previously found that INAVA has dual and competing functions: one at lateral membranes where it affects mucosal barrier function and the other in the cytosol where INAVA enhances IL-1ß signal transduction and protein ubiquitination and forms puncta. We now find that IL-1ß-induced INAVA puncta are biomolecular condensates that rapidly assemble and physiologically resolve. The condensates contain ubiquitin and the E3 ligase ßTrCP2, and their formation correlates with amplified ubiquitination, suggesting function in regulation of cellular proteostasis. Accordingly, a small-molecule screen identified ROS inducers, proteasome inhibitors, and inhibitors of the protein folding chaperone HSP90 as potent agonists for INAVA condensate formation. Notably, inhibitors of the p38α and mTOR pathways enhanced resolution of the condensates, and inhibitors of the Rho-ROCK pathway induced INAVA's competing function by recruiting INAVA to newly assembled intercellular junctions in cells where none existed before.

Active Learning and the Potential of Neural Networks Accelerate Molecular Screening for the Design of a New Molecule Effective against SARS-CoV-2.

A global pandemic has emerged following the appearance of the new severe acute respiratory virus whose official name is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), strongly affecting the health sector as well as the world economy. Indeed, following the emergence of this new virus, despite the existence of a few approved and known effective vaccines at the time of writing this original study, a sense of urgency has emerged worldwide to discover new technical tools and new drugs as soon as possible. In this context, many studies and researches are currently underway to develop new tools and therapies against SARS CoV-2 and other viruses, using different approaches. The 3-chymotrypsin (3CL) protease, which is directly involved in the cotranslational and posttranslational modifications of viral polyproteins essential for the existence and replication of the virus in the host, is one of the coronavirus target proteins that has been the subject of these extensive studies. Currently, the majority of these studies are aimed at repurposing already known and clinically approved drugs against this new virus, but this approach is not really successful. Recently, different studies have successfully demonstrated the effectiveness of artificial intelligence-based techniques to understand existing chemical spaces and generate new small molecules that are both effective and efficient. In this framework and for our study, we combined a generative recurrent neural network model with transfer learning methods and active learning-based algorithms to design novel small molecules capable of effectively inhibiting the 3CL protease in human cells. We then analyze these small molecules to find the correct binding site that matches the structure of the 3CL protease of our target virus as well as other analyses performed in this study. Based on these screening results, some molecules have achieved a good binding score close to -18 kcal/mol, which we can consider as good potential candidates for further synthesis and testing against SARS-CoV-2.

High-content, targeted RNA-seq screening in organoids for drug discovery in colorectal cancer.

Organoids allow the recapitulation of intestinal homeostasis and cancerogenesis in vitro; however, RNA sequencing (RNA-seq)-based methods for drug screens are missing. We develop targeted organoid sequencing (TORNADO-seq), a high-throughput, high-content drug discovery platform that uses targeted RNA-seq to monitor the expression of large gene signatures for the detailed evaluation of cellular phenotypes in organoids. TORNADO-seq is a fast, highly reproducible time- and cost-effective ($5 per sample) method that can probe cell mixtures and their differentiation state in the intestinal system. We apply this method to isolate drugs that enrich for differentiated cell phenotypes and show that these drugs are highly efficacious against cancer compared to wild-type organoids. Furthermore, TORNADO-seq facilitates in-depth insight into the mode of action of these drugs. Our technology can easily be adapted to many other systems and will allow for more systematic, large-scale, and quantitative approaches to study the biology of complex cellular systems.

Identification of a small molecule class to enhance cell-cell adhesion and attenuate prostate tumor growth and metastasis.

Expression of calcitonin (CT) and its receptor (CTR) is elevated in advanced prostate cancer, and activated CT-CTR autocrine axis plays a pivotal role in tumorigenicity and metastatic potential of multiple prostate cancer cell lines. Recent studies suggest that CT promotes prostate cancer metastasis by reducing cell-cell adhesion through the disassembly of tight and adherens junctions and activation of beta-catenin signaling. We attempted to identify a class of molecules that enhances cell-cell adhesion of prostate cells and reverses the disruptive actions of CT on tight and adherens junctions. Screening several compounds led to the emergence of phenyl-methylene hydantoin (PMH) as a lead candidate that can augment cell-cell adhesion and abolish disruptive actions of CT on junctional complexes. PMH reduced invasiveness of PC-3M cells and abolished proinvasive actions of CT. Importantly, PMH did not display significant cytotoxicity on PC-3M cells at the tested doses. I.p. administered PMH and its S-ethyl derivative remarkably decreased orthotopic tumor growth and inhibited the formation of tumor micrometastases in distant organs of nude mice. PMH treatment also reduced the growth of spontaneous tumors in LPB-Tag mice to a significant extent without any obvious cytotoxic effects. By virtue of its ability to stabilize cell junctions, PMH could reverse the effect of CT on junctional disruption and metastasis, which strengthens the possibility of using PMH as a potential drug candidate for CT-positive androgen-independent prostate cancers.

Rapid approach to complex boronic acids.

The compatibility of free boronic acid building blocks in multicomponent reactions to readily create large libraries of diverse and complex small molecules was investigated. Traditionally, boronic acid synthesis is sequential, synthetically demanding, and time-consuming, which leads to high target synthesis times and low coverage of the boronic acid chemical space. We have performed the synthesis of large libraries of boronic acid derivatives based on multiple chemistries and building blocks using acoustic dispensing technology. The synthesis was performed on a nanomole scale with high synthesis success rates. The discovery of a protease inhibitor underscores the usefulness of the approach. Our acoustic dispensing-enabled chemistry paves the way to highly accelerated synthesis and miniaturized reaction scouting, allowing access to unprecedented boronic acid libraries.

Preparation and infrared/raman classification of 630 spectroscopically encoded styrene copolymers.

The barcoded resins (BCRs) were introduced recently as a platform for encoded combinatorial chemistry. One of the main challenges yet to be overcome is the demonstration that a large number of BCRs could be generated and classified with high confidence. Here, we describe the synthesis and classification of 630 polystyrene-based copolymers prepared from the combinatorial association of 15 spectroscopically active styrene monomers. Each of the 630 copolymers displayed a unique vibrational fingerprint (infrared and Raman), which was converted into a spectral vector. To each of the 630 copolymers, a vector of the known (reference) composition was assigned. Unknown (prediction) vectors were decoded using multivariate data analysis. From the inner product of the reference and prediction vectors, a correlation map comparing 396 900 copolymer pairs (630 x 630) was generated. In 100% of the cases, the highest correlation was obtained for polymer pairs in which the reference and prediction vectors correspond to copolymers prepared from identical styrene monomers, thus demonstrating the high reliability of this encoding strategy. We have also established that the spectroscopic barcodes generated from the Raman and infrared spectra are independent of the copolymers' morphology (beaded versus bulk polymers). Besides the demonstration of the generality of the polymer barcoding strategy, the analytical methods developed here could in principle be extended to the investigation of the composition and purity of any other synthetic polymer and biopolymer library, or even scaffold-based combinatorial libraries.

Analysing and Navigating Natural Products Space for Generating Small, Diverse, But Representative Chemical Libraries.

Armed with the digital availability of two natural products libraries, amounting to some 195 885 molecular entities, we ask the question of how we can best sample from them to maximize their "representativeness" in smaller and more usable libraries of 96, 384, 1152, and 1920 molecules. The term "representativeness" is intended to include diversity, but for numerical reasons (and the likelihood of being able to perform a QSAR) it is necessary to focus on areas of chemical space that are more highly populated. Encoding chemical structures as fingerprints using the RDKit "patterned" algorithm, we first assess the granularity of the natural products space using a simple clustering algorithm, showing that there are major regions of "denseness" but also a great many very sparsely populated areas. We then apply a "hybrid" hierarchical K-means clustering algorithm to the data to produce more statistically robust clusters from which representative and appropriate numbers of samples may be chosen. There is necessarily again a trade-off between cluster size and cluster number, but within these constraints, libraries containing 384 or 1152 molecules can be found that come from clusters that represent some 18 and 30% of the whole chemical space, with cluster sizes of, respectively, 50 and 27 or above, just about sufficient to perform a QSAR. By using the online availability of molecules via the Molport system (www.molport.com), we are also able to construct (and, for the first time, provide the contents of) a small virtual library of available molecules that provided effective coverage of the chemical space described. Consistent with this, the average molecular similarities of the contents of the libraries developed is considerably smaller than is that of the original libraries. The suggested libraries may have use in molecular or phenotypic screening, including for determining possible transporter substrates.

Manipulation of Hematopoietic Stem Cell Fate by Small Molecule Compounds.

Self-renewal and multipotential differentiation are two important features of hematopoietic stem/progenitor cells (HS/PCs) that make them as an ideal source of stem cells for treatment of many hematologic disorders and cancers. Regarding the limited number of cord blood HS/PCs, proper ex vivo expansion can significantly increase the clinical use of cord blood stem cells. Meanwhile, expansion of HS/PCs will be feasible through bypassing the quiescent state of HS/PCs and simultaneously enhancing their proliferative potential and survival while delaying the terminal differentiation and exhaustion. Previous investigations have demonstrated that defined sets of exogenous hematopoietic cytokines/growth factors such as stem cell factor, Flt-3 ligand, and thrombopoietin are able to expand HS/PCs. However, in recent years, small molecule compounds (SMCs) have emerged as a powerful tool for the effective expansion of HS/PCs by modulating multiple cellular processes including different signaling pathways and epigenetics. In this review, recent progress toward the use of SMCs in HS cell research is presented. We focus on the significant applications of SMCs related to HS/PC expansion and discuss the associated mechanism. At the end we present a list of those SMCs which enter to clinical trials.