Objective, Quantitative, Data-Driven Assessment of Chemical Probes

Computational repurposing of oncology drugs through off-target drug binding interactions from pharmacological databases

PURPOSE

Systematic repurposing of approved medicines for another indication may accelerate drug development in oncology. We present a strategy combining biomarker testing with drug repurposing to identify new treatments for patients with advanced cancer.

METHODS

Tumours were sequenced with the Illumina TruSight Oncology 500 (TSO-500) platform or the FoundationOne CDx panel. Mutations were screened by two medical oncologists and pathogenic mutations were categorised referencing literature. Variants of unknown significance were classified as potentially pathogenic using plausible mechanisms and computational prediction of pathogenicity. Gain of function (GOF) mutations were evaluated through repurposing databases Probe Miner (PM), Broad Institute Drug Repurposing Hub (Broad Institute DRH) and TOPOGRAPH. GOF mutations were repurposing events if identified in PM, not indexed in TOPOGRAPH and excluding mutations with a known Food and Drug Administration (FDA)-approved biomarker. The computational repurposing approach was validated by evaluating its ability to identify FDA-approved biomarkers. The total repurposable genome was identified by evaluating all possible gene-FDA drug-approved combinations in the PM dataset.

RESULTS

The computational repurposing approach was accurate at identifying FDA therapies with known biomarkers (94%). Using next-generation sequencing molecular reports (n = 94), a meaningful percentage of patients (14%) could have an off-label therapeutic identified. The frequency of theoretical drug repurposing events in The Cancer Genome Atlas pan-cancer dataset was 73% of the samples in the cohort.

CONCLUSION

A computational drug repurposing approach may assist in identifying novel repurposing events in cancer patients with no access to standard therapies. Further validation is needed to confirm a precision oncology approach using drug repurposing.

Proteomics-Based Discovery of First-in-Class Chemical Probes for Programmed Cell Death Protein 2 (PDCD2)

Chemical probes are essential tools for understanding biological systems and for credentialing potential biomedical targets. Programmed cell death 2 (PDCD2) is a member of the B-cell lymphoma 2 (Bcl-2) family of proteins, which are critical regulators of apoptosis. Here we report the discovery and characterization of 10 e, a first-in-class small molecule degrader of PDCD2. We discovered this PDCD2 degrader by serendipity using a chemical proteomics approach, in contrast to the conventional approach for making bivalent degraders starting from a known binding ligand targeting the protein of interest. Using 10 e as a pharmacological probe, we demonstrate that PDCD2 functions as a critical regulator of cell growth by modulating the progression of the cell cycle in T lymphoblasts. Our work provides a useful pharmacological probe for investigating PDCD2 function and highlights the use of chemical proteomics to discover selective small molecule degraders of unanticipated targets.

Which Small Molecule? Selecting Chemical Probes for Use in Cancer Research and Target Validation

Small-molecule chemical "probes" complement the use of molecular biology techniques to explore, validate, and generate hypotheses on the function of proteins in diseases such as cancer. Unfortunately, the poor selection and use of small-molecule reagents can lead to incorrect conclusions. Here, we illustrate examples of poor chemical tools and suggest best practices for the selection, validation, and use of high-quality chemical probes in cancer research. We also note the complexity associated with tools for novel drug modalities, exemplified by protein degraders, and provide advice and resources to facilitate the independent identification of appropriate small-molecule probes by researchers.

SIGNIFICANCE

Validation of biological targets and pathways will be aided by a shared understanding of the criteria of potency, selectivity, and target engagement associated with small-molecule reagents ("chemical probes") that enable that work. Interdisciplinary collaboration between cancer biologists, medicinal chemists, and chemical biologists and the awareness of available resources will reduce misleading data generation and interpretation, strengthen data robustness, and improve productivity in academic and industrial research.

Integrative GWAS and co-localisation analysis suggests novel genes associated with age-related multimorbidity

Advancing age is the greatest risk factor for developing multiple age-related diseases. Therapeutic approaches targeting the underlying pathways of ageing, rather than individual diseases, may be an effective way to treat and prevent age-related morbidity while reducing the burden of polypharmacy. We harness the Open Targets Genetics Portal to perform a systematic analysis of nearly 1,400 genome-wide association studies (GWAS) mapped to 34 age-related diseases and traits, identifying genetic signals that are shared between two or more of these traits. Using locus-to-gene (L2G) mapping, we identify 995 targets with shared genetic links to age-related diseases and traits, which are enriched in mechanisms of ageing and include known ageing and longevity-related genes. Of these 995 genes, 128 are the target of an approved or investigational drug, 526 have experimental evidence of binding pockets or are predicted to be tractable, and 341 have no existing tractability evidence, representing underexplored genes which may reveal novel biological insights and therapeutic opportunities. We present these candidate targets for exploration and prioritisation in a web application.

Effectively utilizing publicly available databases for cancer target evaluation

The majority of compounds designed against cancer drug targets do not progress to become approved drugs, mainly due to lack of efficacy and/or unmanageable toxicity. Robust target evaluation is therefore required before progressing through the drug discovery process to reduce the high attrition rate. There are a wealth of publicly available databases that can be mined to generate data as part of a target evaluation. It can, however, be challenging to learn what databases are available, how and when they should be used, and to understand the associated limitations. Here, we have compiled and present key, freely accessible and easy-to-use databases that house informative datasets from in vitro, in vivo and clinical studies. We also highlight comprehensive target review databases that aim to bring together information from multiple sources into one-stop portals. In the post-genomics era, a key objective is to exploit the extensive cell, animal and patient characterization datasets in order to deliver precision medicine on a patient-specific basis. Effective utilization of the highlighted databases will go some way towards supporting the cancer research community achieve these aims.

iTAG an optimized IMiD-induced degron for targeted protein degradation in human and murine cells

To address the limitation associated with degron based systems, we have developed iTAG, a synthetic tag based on IMiDs/CELMoDs mechanism of action that improves and addresses the limitations of both PROTAC and previous IMiDs/CeLMoDs based tags. Using structural and sequence analysis, we systematically explored native and chimeric degron containing domains (DCDs) and evaluated their ability to induce degradation. We identified the optimal chimeric iTAG(DCD23 60aa) that elicits robust degradation of targets across cell types and subcellular localizations without exhibiting the well documented "hook effect" of PROTAC-based systems. We showed that iTAG can also induce target degradation by murine CRBN and enabled the exploration of natural neo-substrates that can be degraded by murine CRBN. Hence, the iTAG system constitutes a versatile tool to degrade targets across the human and murine proteome.

Systematic literature review reveals suboptimal use of chemical probes in cell-based biomedical research

Chemical probes have reached a prominent role in biomedical research, but their impact is governed by experimental design. To gain insight into the use of chemical probes, we conducted a systematic review of 662 publications, understood here as primary research articles, employing eight different chemical probes in cell-based research. We summarised (i) concentration(s) at which chemical probes were used in cell-based assays, (ii) inclusion of structurally matched target-inactive control compounds and (iii) orthogonal chemical probes. Here, we show that only 4% of analysed eligible publications used chemical probes within the recommended concentration range and included inactive compounds as well as orthogonal chemical probes. These findings indicate that the best practice with chemical probes is yet to be implemented in biomedical research. To achieve this, we propose 'the rule of two': At least two chemical probes (either orthogonal target-engaging probes, and/or a pair of a chemical probe and matched target-inactive compound) to be employed at recommended concentrations in every study.

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.

Proteome-Wide Fragment-Based Ligand and Target Discovery

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

Target 2035 – an update on private sector contributions

Target 2035, an international federation of biomedical scientists from the public and private sectors, is leveraging ‘open’ principles to develop a pharmacological tool for every human protein.

The era of high-quality chemical probes

Small-molecule chemical probes are among the most important tools to study the function of proteins in cells and organisms. Regrettably, the use of weak and non-selective small molecules has generated an abundance of erroneous conclusions in the scientific literature. More recently, minimal criteria have been outlined for investigational compounds, encouraging the selection and use of high-quality chemical probes. Here, we briefly recall the milestones and key initiatives that have paved the way to this new era, illustrate examples of recent high-quality chemical probes and provide our perspective on future challenges and developments.

canSAR chemistry registration and standardization pipeline

Background

Integration of medicinal chemistry data from numerous public resources is an increasingly important part of academic drug discovery and translational research because it can bring a wealth of important knowledge related to compounds in one place. However, different data sources can report the same or related compounds in various forms (e.g., tautomers, racemates, etc.), thus highlighting the need of organising related compounds in hierarchies that alert the user on important bioactivity data that may be relevant. To generate these compound hierarchies, we have developed and implemented canSARchem, a new compound registration and standardization pipeline as part of the canSAR public knowledgebase. canSARchem builds on previously developed ChEMBL and PubChem pipelines and is developed using KNIME. We describe the pipeline which we make publicly available, and we provide examples on the strengths and limitations of the use of hierarchies for bioactivity data exploration. Finally, we identify canonicalization enrichment in FDA-approved drugs, illustrating the benefits of our approach.

Results

We created a chemical registration and standardization pipeline in KNIME and made it freely available to the research community. The pipeline consists of five steps to register the compounds and create the compounds’ hierarchy: 1. Structure checker, 2. Standardization, 3. Generation of canonical tautomers and representative structures, 4. Salt strip, and 5. Generation of abstract structure to generate the compound hierarchy. Unlike ChEMBL’s RDKit pipeline, we carry out compound canonicalization ahead of getting the parent structure, similar to PubChem’s OpenEye pipeline. canSARchem has a lower rejection rate compared to both PubChem and ChEMBL. We use our pipeline to assess the impact of grouping the compounds in hierarchies for bioactivity data exploration. We find that FDA-approved drugs show statistically significant sensitivity to canonicalization compared to the majority of bioactive compounds which demonstrates the importance of this step.

Conclusions

We use canSARchem to standardize all the compounds uploaded in canSAR (> 3 million) enabling efficient data integration and the rapid identification of alternative compound forms with useful bioactivity data. Comparison with PubChem and ChEMBL pipelines evidenced comparable performances in compound standardization, but only PubChem and canSAR canonicalize tautomers and canSAR has a slightly lower rejection rate. Our results highlight the importance of compound hierarchies for bioactivity data exploration. We make canSARchem available under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) at https://gitlab.icr.ac.uk/cansar-public/compound-registration-pipeline.

AD Informer Set: Chemical tools to facilitate Alzheimer's disease drug discovery

Introduction

The portfolio of novel targets to treat Alzheimer's disease (AD) has been enriched by the Accelerating Medicines Partnership Program for Alzheimer's Disease (AMP AD) program.

Methods

Publicly available resources, such as literature and databases, enabled a data-driven effort to identify existing small molecule modulators for many protein products expressed by the genes nominated by AMP AD and suitable positive control compounds to be included in the set. Compounds contained within the set were manually selected and annotated with associated published, predicted, and/or experimental data.

Results

We built an annotated set of 171 small molecule modulators targeting 98 unique proteins that have been nominated by AMP AD consortium members as novel targets for the treatment of AD. The majority of compounds included in the set are inhibitors. These small molecules vary in their quality and should be considered chemical tools that can be used in efforts to validate therapeutic hypotheses, but which will require further optimization. A physical copy of the AD Informer Set can be requested on the Target Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT-AD) website.

Discussion

Small molecules that enable target validation are important tools for the translation of novel hypotheses into viable therapeutic strategies for AD.

Validating Small Molecule Chemical Probes for Biological Discovery

Small molecule chemical probes are valuable tools for interrogating protein biological functions and relevance as a therapeutic target. Rigorous validation of chemical probe parameters such as cellular potency and selectivity is critical to unequivocally linking biological and phenotypic data resulting from treatment with a chemical probe to the function of a specific target protein. A variety of modern technologies are available to evaluate cellular potency and selectivity, target engagement, and functional response biomarkers of chemical probe compounds. Here, we review these technologies and the rationales behind using them for the characterization and validation of chemical probes. In addition, large-scale phenotypic characterization of chemical probes through chemical genetic screening is increasingly leading to a wealth of information on the cellular pharmacology and disease involvement of potential therapeutic targets. Extensive compound validation approaches and integration of phenotypic information will lay foundations for further use of chemical probes in biological discovery. Expected final online publication date for the Annual Review of Biochemistry, Volume 91 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Target 2035 - update on the quest for a probe for every protein

Twenty years after the publication of the first draft of the human genome, our knowledge of the human proteome is still fragmented. The challenge of translating the wealth of new knowledge from genomics into new medicines is that proteins, and not genes, are the primary executers of biological function. Therefore, much of how biology works in health and disease must be understood through the lens of protein function. Accordingly, a subset of human proteins has been at the heart of research interests of scientists over the centuries, and we have accumulated varying degrees of knowledge about approximately 65% of the human proteome. Nevertheless, a large proportion of proteins in the human proteome (∼35%) remains uncharacterized, and less than 5% of the human proteome has been successfully targeted for drug discovery. This highlights the profound disconnect between our abilities to obtain genetic information and subsequent development of effective medicines. Target 2035 is an international federation of biomedical scientists from the public and private sectors, which aims to address this gap by developing and applying new technologies to create by year 2035 chemogenomic libraries, chemical probes, and/or biological probes for the entire human proteome.

Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.

Polypharmacology of clinical sodium glucose co-transport protein 2 inhibitors and relationship to suspected adverse drug reactions

Sodium glucose co-transporter 2 inhibitors (SGLT2i) are a promising second-line treatment strategy for type 2 diabetes mellitus (T2DM) with a developing landscape of both beneficial cardio- and nephroprotective properties and emerging adverse drug reactions (ADRs) including diabetic ketoacidosis (DKA), genetic mycotic infections, and amputations among others. A national register study (MHRA Yellow Card, UK) was used to quantify the SGLT2i's suspected ADRs relative to their Rx rate (OpenPrescribing, UK). The polypharmacology profiles of SGLT2i were data-mined (ChEMBL) for the first time. The ADR reports (n = 3629) and prescribing numbers (Rx n = 5,813,325) for each SGLT2i in the United Kingdom (from launch date to the beginning December 2019) were determined. Empagliflozin possesses the most selective SGLT2/SGLT1 inhibition profile at ~2500-fold, ~10-fold more selective than cangliflozin (~260-fold). Canagliflozin was found to also inhibit CYP at clinically achievable concentrations. We find that for overall ADR rates, empagliflozin versus dapagliflozin and empagliflozin versus canagliflozin are statistically significant (χ2 , p < .05), while dapagliflozin versus canagliflozin is not. In terms of overall ADRs, there is a greater relative rate for canagliflozin > dapagliflozin > empagliflozin. For fatalities, there is a greater relative rate for dapagliflozin > canagliflozin > empagliflozin. An organ classification that resulted in a statistically significant difference between SGLT2i was suspected infection/infestation ADRs between empagliflozin and dapagliflozin. Our findings at this stage of SGLT2i usage in the United Kingdom suggest that empagliflozin, the most selective SGLT2i, had the lowest suspected ADR incident rate (relative to prescribing) and in all reported classes of ADRs identified including infections, amputations, and DKA.

Will the chemical probes please stand up?

In 2005, the NIH Molecular Libraries Program (MLP) undertook the identification of tool compounds to expand biological insights, now termed small-molecule chemical probes. This inspired other organisations to initiate similar efforts from 2010 onwards. As a central focus of the Probes & Drugs portal (P&D), we have standardised, integrated and compared sets of declared probe compounds harvested from 12 different sources. This turned out to be challenging and revealed unexpected anomalies. Results in this work address key questions including; a) individual and total structure counts, b) overlaps between sources, c) comparisons with selected PubChem sources and d) investigating the probe coverage of druggable targets. In addition, we developed new high-level scoring schemes to filter collections down to probes of higher quality. This generated 548 high-quality chemical probes (HQCP) covering 447 distinct protein targets. This HQCP collection has been added to the P&D portal and will be regularly updated as established sources expand and new ones release data.

Identification of different side effects between PARP inhibitors and their polypharmacological multi-target rationale

AIMS

The aim of this study was to determine the differences and potential mechanistic rationale for observed adverse drug reactions (ADRs) between four approved PARP inhibitors (PARPi).

METHODS

The Medicines and Healthcare products Regulatory Authority (MHRA) Yellow Card drug analysis profiles and NHS secondary care medicines database enabled the identification of suspected ADRs associated with the PARPi in the UK from launch to 2020. The polypharmacology of the PARPi were data-mined from several public data sources.

RESULTS

The overall ADRs per 100 000 R_x identified across the four PARPi are statistically significant (χ² test, P < .001). Rucaparib has the greatest relative suspected ADRs, which can be explained by its least clean kinome and physicochemical properties. The suspected gastrointestinal ADRs of rucaparib and niraparib can be ascribed to their kinase polypharmacology. Suspected blood and lymphatic system ADRs of PARPi can be linked to their high volume of distribution (V_d ). The thrombocytopenia rate of niraparib > rucaparib > olaparib tracked with the V_d trend. Hypertension is only associated with niraparib and could be explained by the therapeutically achievable inhibition of DYRK1A and/or transporters. Arrhythmia cases are potentially linked to the structural features of hERG ion-channel inhibition found in rucaparib and niraparib. Enhanced psychiatric/nervous disorders associated with niraparib can be interpreted from the diverse neurotransporter off-targets reported.

CONCLUSIONS

Despite their similar mode of action, the differential polypharmacology of PARP inhibitors influences their ADR profile.

Discovery of a Cell-Active SuTEx Ligand of Prostaglandin Reductase 2

Sulfonyl-triazoles have emerged as a new reactive group for covalent modification of tyrosine sites on proteins through sulfur-triazole exchange (SuTEx) chemistry. The extent to which this sulfur electrophile can be tuned for developing ligands with cellular activity remains largely underexplored. Here, we performed fragment-based ligand discovery in live cells to identify SuTEx compounds capable of liganding tyrosine sites on diverse protein targets. We verified our quantitative chemical proteomic findings by demonstrating concentration-dependent activity of SuTEx ligands, but not inactive counterparts, against recombinant protein targets directly in live cells. Our structure-activity relationship studies identified the SuTEx ligand HHS-0701 as a cell-active inhibitor capable of blocking prostaglandin reductase 2 (PTGR2) biochemical activity.

Functionalized Scout Fragments for Site-Specific Covalent Ligand Discovery and Optimization

Covalent ligands are a versatile class of chemical probes and drugs that can target noncanonical sites on proteins and display differentiated pharmacodynamic properties. Chemical proteomic methods have been introduced that leverage electrophilic fragments to globally profile the covalent ligandability of nucleophilic residues, such as cysteine and lysine, in native biological systems. Further optimization of these initial ligandability events without resorting to the time-consuming process of individualized protein purification and functional assay development, however, presents a persistent technical challenge. Here, we show that broadly reactive electrophilic fragments, or "scouts", can be converted into site-specific target engagement probes for screening small molecules against a wide array of proteins in convenient gel- and ELISA-based assay formats. We use these assays to expediently optimize a weak potency fragment hit into a sub-μM inhibitor that selectively engages an active-site cysteine in the retinaldehyde reductase AKR1B10. Our findings provide a road map to optimize covalent fragments into more advanced chemical probes without requiring protein purification or structural analysis.

The Promise and Peril of Chemical Probe Negative Controls

Chemical probes are selective modulators that are used in cell assays to link a phenotype to a gene and have become indispensable tools to explore gene function and discover therapeutic targets. Chemical probe off-targets are a confounding factor as the observed phenotype may be driven by inhibition of an unknown off-target instead of the targeted protein. A negative control, a close chemical analog of the chemical probe that is inactive against the intended target, is typically used to verify that the phenotype is indeed driven by the targeted protein. Here, we compare the selectivity profiles of four unrelated chemical probes and their respective negative controls. We find that controls that chemically deviate from the probe by a single heavy atom can be inactive against up to 80% of known off-targets if the chemical modification has a charge-neutralizing effect. In such cases, a loss in phenotype upon treatment with the negative control may be driven by loss of inhibition of an off-target. To expand this analysis, we inspect the crystal structures of 90 pairs of unrelated proteins, where both proteins within each pair is in complex with the same drug-like ligand. We computationally estimate that in 50% of cases, methylation of the ligand (a simple chemical modification often used to generate negative controls) at a position that will preclude binding to one protein (the intended target) will also preclude binding to the other (the off-target). These results emphasize the need to select negative controls with care and profile both chemical probes and negative controls against diverse protein arrays to verify that off-targets of probes are also hit by negative controls. When available, a best practice should be to verify that two unrelated chemical probes targeting the same protein elicit the same phenotype.

Chiral figure-eight molecular scaffold for fluorescent probe development

Targeted fluorescent molecular probes are useful for cell microscopy, diagnostics, and biological imaging. An emerging discovery paradigm is to screen libraries of fluorescent molecules and identify hit compounds with interesting targeting properties. However, a current limitation with this approach is the lack of fluorescent molecular scaffolds that can produce libraries of probe candidates with three dimensional globular shape, chiral centers, and constrained conformation. This study evaluated a new probe scaffold called squaraine figure-eight (SF8), a self-threaded molecular architecture that is comprised of an encapsulated deep-red fluorescent squaraine dye, surrounding tetralactam macrocycle, and peripheral loops. Easy synthetic variation of the loops produced four chiral isomeric SF8 probes, with the same log P values. Cell microscopy showed that subtle changes in the loop structure led to significant differences in intracellular targeting. Most notably, a comparison of enantiomeric probes revealed a large difference in mitochondrial accumulation, very likely due to differences in affinity for a chiral biomarker within the organelle. A tangible outcome of the research is a probe candidate that can be: (a) developed further as a bright and photostable, deep-red fluorescent probe for mitochondrial imaging, and (b) used as a molecular tool to identify the mitochondrial biomarker for selective targeting. It will be straightforward to expand the SF8 probe chemical space and produce structurally diverse probe libraries with high potential for selective targeting of a wide range of biomarkers.

Public resources for chemical probes: the journey so far and the road ahead

High-quality small molecule chemical probes are extremely valuable for biological research and target validation. However, frequent use of flawed small-molecule inhibitors produces misleading results and diminishes the robustness of biomedical research. Several public resources are available to facilitate assessment and selection of better chemical probes for specific protein targets. Here, we review chemical probe resources, discuss their current strengths and limitations, and make recommendations for further improvements. Expert review resources provide in-depth analysis but currently cover only a limited portion of the liganded proteome. Computational resources encompass more proteins and are regularly updated, but have limitations in data availability and curation. We show how biomedical scientists may use these resources to choose the best available chemical probes for their research.

Highly selective inhibitors of protein kinases CLK and HIPK with the furo[3,2-b]pyridine core

The furo [3,2-b]pyridine motif represents a relatively underexplored central pharmacophore in the area of kinase inhibitors. Herein, we report flexible synthesis of 3,5-disubstituted furo [3,2-b]pyridines that relies on chemoselective couplings of newly prepared 5-chloro-3-iodofuro [3,2-b]pyridine. This methodology allowed efficient second-generation synthesis of the state-of-the-art chemical biology probe for CLK1/2/4 MU1210, and identification of the highly selective inhibitors of HIPKs MU135 and MU1787 which are presented and characterized in this study, including the X-ray crystal structure of MU135 in HIPK2. chemical biology probe.

canSAR: update to the cancer translational research and drug discovery knowledgebase

canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-informed target assessment for drug discovery. canSAR is widely used internationally by academia and industry. Here we describe significant developments and enhancements to the data, web interface and infrastructure of canSAR in the form of the new implementation of the system: canSARblack. We demonstrate new functionality in aiding translation hypothesis generation and experimental design, and show how canSAR can be adapted and utilised outside oncology.

Improving target assessment in biomedical research: the GOT-IT recommendations

Academic research plays a key role in identifying new drug targets, including understanding target biology and links between targets and disease states. To lead to new drugs, however, research must progress from purely academic exploration to the initiation of efforts to identify and test a drug candidate in clinical trials, which are typically conducted by the biopharma industry. This transition can be facilitated by a timely focus on target assessment aspects such as target-related safety issues, druggability and assayability, as well as the potential for target modulation to achieve differentiation from established therapies. Here, we present recommendations from the GOT-IT working group, which have been designed to support academic scientists and funders of translational research in identifying and prioritizing target assessment activities and in defining a critical path to reach scientific goals as well as goals related to licensing, partnering with industry or initiating clinical development programmes. Based on sets of guiding questions for different areas of target assessment, the GOT-IT framework is intended to stimulate academic scientists' awareness of factors that make translational research more robust and efficient, and to facilitate academia-industry collaboration.

Enhancing Chemogenomics with Predictive Pharmacology

One of the grand challenges in contemporary chemical biology is the generation of a probe for every member of the human proteome. Probe selection and optimization strategies typically rely on experimental bioactivity data to determine the potency and selectivity of candidate molecules. However, this approach is profoundly limited by the sparsity of the known data, the annotation bias often found in the literature, and the cost of physical screening. Recent advancements in predictive pharmacology, such as the application of multitask and transfer learning, as well as the use of biologically motivated, structure-agnostic features to characterize molecules, should serve to mitigate these issues. Computational modeling likely offers the only cost-effective approach to substantially increasing the bioactivity annotation density both on the local and global scale and thus, we argue, will need to make a substantial contribution if the ambitious goals of probing the human proteome are to be realized in the foreseeable future.

Artificial Intelligence Effecting a Paradigm Shift in Drug Development

The inverse relationship between the cost of drug development and the successful integration of drugs into the market has resulted in the need for innovative solutions to overcome this burgeoning problem. This problem could be attributed to several factors, including the premature termination of clinical trials, regulatory factors, or decisions made in the earlier drug development processes. The introduction of artificial intelligence (AI) to accelerate and assist drug development has resulted in cheaper and more efficient processes, ultimately improving the success rates of clinical trials. This review aims to showcase and compare the different applications of AI technology that aid automation and improve success in drug development, particularly in novel drug target identification and design, drug repositioning, biomarker identification, and effective patient stratification, through exploration of different disease landscapes. In addition, it will also highlight how these technologies are translated into the clinic. This paradigm shift will lead to even greater advancements in the integration of AI in automating processes within drug development and discovery, enabling the probability and reality of attaining future precision and personalized medicine.

Designing custom CRISPR libraries for hypothesis-driven drug target discovery

Over the last decade Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) has been developed into a potent molecular biology tool used to rapidly modify genes or their expression in a multitude of ways. In parallel, CRISPR-based screening approaches have been developed as powerful discovery platforms for dissecting the genetic basis of cellular behavior, as well as for drug target discovery. CRISPR screens can be designed in numerous ways. Here, we give a brief background to CRISPR screens and discuss the pros and cons of different design approaches, including unbiased genome-wide screens that target all known genes, as well as hypothesis-driven custom screens in which selected subsets of genes are targeted (Fig. 1). We provide several suggestions for how a custom screen can be designed, which could broadly serve as inspiration for any experiment that includes candidate gene selection. Finally, we discuss how results from CRISPR screens could be translated into drug development, as well as future trends we foresee in the rapidly evolving CRISPR screen field.

Inducers of the endothelial cell barrier identified through chemogenomic screening in genome-edited hPSC-endothelial cells

The blood-retina barrier and blood-brain barrier (BRB/BBB) are selective and semipermeable and are critical for supporting and protecting central nervous system (CNS)-resident cells. Endothelial cells (ECs) within the BRB/BBB are tightly coupled, express high levels of Claudin-5 (CLDN5), a junctional protein that stabilizes ECs, and are important for proper neuronal function. To identify novel CLDN5 regulators (and ultimately EC stabilizers), we generated a CLDN5-P2A-GFP stable cell line from human pluripotent stem cells (hPSCs), directed their differentiation to ECs (CLDN5-GFP hPSC-ECs), and performed flow cytometry-based chemogenomic library screening to measure GFP expression as a surrogate reporter of barrier integrity. Using this approach, we identified 62 unique compounds that activated CLDN5-GFP. Among them were TGF-β pathway inhibitors, including RepSox. When applied to hPSC-ECs, primary brain ECs, and retinal ECs, RepSox strongly elevated barrier resistance (transendothelial electrical resistance), reduced paracellular permeability (fluorescein isothiocyanate-dextran), and prevented vascular endothelial growth factor A (VEGFA)-induced barrier breakdown in vitro. RepSox also altered vascular patterning in the mouse retina during development when delivered exogenously. To determine the mechanism of action of RepSox, we performed kinome-, transcriptome-, and proteome-profiling and discovered that RepSox inhibited TGF-β, VEGFA, and inflammatory gene networks. In addition, RepSox not only activated vascular-stabilizing and barrier-establishing Notch and Wnt pathways, but also induced expression of important tight junctions and transporters. Taken together, our data suggest that inhibiting multiple pathways by selected individual small molecules, such as RepSox, may be an effective strategy for the development of better BRB/BBB models and novel EC barrier-inducing therapeutics.

The chemical probe - scopes, limitations and challenges

Drug design needs high-quality chemical probes for target validation, but the demands on chemical probes are largely different than those on drugs. Whereas therapeutic value and safety are main criteria for a drug evaluation, the chemical probe is influencing a biological target in a well-characterized way. Affinity, efficacy, selectivity and versatility in different read-outs are main criteria for chemical probes to test biochemical hypothesis and verify targets for new therapeutic approaches.

Discovery of a selective inhibitor of doublecortin like kinase 1

Doublecortin like kinase 1 (DCLK1) is an understudied kinase that is upregulated in a wide range of cancers, including pancreatic ductal adenocarcinoma (PDAC). However, little is known about its potential as a therapeutic target. We used chemoproteomic profiling and structure-based design to develop a selective, in vivo-compatible chemical probe of the DCLK1 kinase domain, DCLK1-IN-1. We demonstrate activity of DCLK1-IN-1 against clinically relevant patient-derived PDAC organoid models and use a combination of RNA-sequencing, proteomics and phosphoproteomics analysis to reveal that DCLK1 inhibition modulates proteins and pathways associated with cell motility in this context. DCLK1-IN-1 will serve as a versatile tool to investigate DCLK1 biology and establish its role in cancer.

Open Targets Platform: new developments and updates two years on

The Open Targets Platform integrates evidence from genetics, genomics, transcriptomics, drugs, animal models and scientific literature to score and rank target-disease associations for drug target identification. The associations are displayed in an intuitive user interface (https://www.targetvalidation.org), and are available through a REST-API (https://api.opentargets.io/v3/platform/docs/swagger-ui) and a bulk download (https://www.targetvalidation.org/downloads/data). In addition to target-disease associations, we also aggregate and display data at the target and disease levels to aid target prioritisation. Since our first publication two years ago, we have made eight releases, added new data sources for target-disease associations, started including causal genetic variants from non genome-wide targeted arrays, added new target and disease annotations, launched new visualisations and improved existing ones and released a new web tool for batch search of up to 200 targets. We have a new URL for the Open Targets Platform REST-API, new REST endpoints and also removed the need for authorisation for API fair use. Here, we present the latest developments of the Open Targets Platform, expanding the evidence and target-disease associations with new and improved data sources, refining data quality, enhancing website usability, and increasing our user base with our training workshops, user support, social media and bioinformatics forum engagement.

Reflections and Outlook on Targeting HSP90, HSP70 and HSF1 in Cancer: A Personal Perspective

This personal perspective focuses on small-molecule inhibitors of proteostasis networks in cancer-specifically the discovery and development of chemical probes and drugs acting on the molecular chaperones HSP90 and HSP70, and on the HSF1 stress pathway. Emphasis is on progress made and lessons learned and a future outlook is provided. Highly potent, selective HSP90 inhibitors have proved invaluable in exploring the role of this molecular chaperone family in biology and disease pathology. Clinical activity was observed, especially in non small cell lung cancer and HER2 positive breast cancer. Optimal use of HSP90 inhibitors in oncology will likely require development of creative combination strategies. HSP70 family members have proved technically harder to drug. However, recent progress has been made towards useful chemical tool compounds and these may signpost future clinical drug candidates. The HSF1 stress pathway is strongly validated as a target for cancer therapy. HSF1 itself is a ligandless transcription factor that is extremely challenging to drug directly. HSF1 pathway inhibitors have been identified mostly by phenotypic screening, including a series of bisamides from which a clinical candidate has been identified for treatment of ovarian cancer, multiple myeloma and potentially other cancers.

Prediction of Protein-Ligand Interaction Based on the Positional Similarity Scores Derived from Amino Acid Sequences

The affinity of different drug-like ligands to multiple protein targets reflects general chemical–biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein–ligand interactions based on pairwise sequence alignment provides reasonable accuracy if the ligands’ specificity well coincides with the phylogenic taxonomy of the proteins. Methods using multiple alignment require an accurate match of functionally significant residues. Such conditions may not be met in the case of diverged protein families. To overcome these limitations, we propose an approach based on the analysis of local sequence similarity within the set of analyzed proteins. The positional scores, calculated by sequence fragment comparisons, are used as input data for the Bayesian classifier. Our approach provides a prediction accuracy comparable or exceeding those of other methods. It was demonstrated on the popular Gold Standard test sets, presenting different sequence heterogeneity and varying from the group, including different protein families to the more specific groups. A reasonable prediction accuracy was also found for protein kinases, displaying weak relationships between sequence phylogeny and inhibitor specificity. Thus, our method can be applied to the broad area of protein–ligand interactions.

Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification

Efficient identification of chemical probes for the manipulation and understanding of biological systems demands specificity for target proteins. Computational means to optimize candidate compound selection for experimental selectivity evaluation are being sought. The active learning virtual screening method has demonstrated the ability to efficiently converge on predictive models with reduced datasets, though its applicability domain to probe identification has yet to be determined. In this article, we challenge active learning’s ability to predict inhibitory bioactivity profiles of selective compounds when learning from chemogenomic features found in non-selective ligand-target pairs. Comparison of controls versus multiple molecule representations de-convolutes factors contributing to predictive capability. Experiments using the matrix metalloproteinase family demonstrate maximum probe bioactivity prediction achieved from only approximately 20% of non-probe bioactivity; this data volume is consistent with prior chemogenomic active learning studies despite the increased difficulty from chemical biology experimental settings used here. Feature weight analyses are combined with a custom visualization to unambiguously detail how active learning arrives at classification decisions, yielding clarified expectations for chemogenomic modeling. The results influence tactical decisions for computational probe design and discovery.

Labelled chemical probes for demonstrating direct target engagement in living systems

Demonstrating target engagement in living systems can help drive successful drug discovery. Target engagement and occupancy studies in cells confirm direct binding of a ligand to its intended target protein and provide the binding affinity. Combined with biomarkers to measure the functional consequences of target engagement, these experiments can increase confidence in the relationship between in vitro pharmacology and observed biological effects. In this review, we focus on chemically and radioactively labelled probes as key reagents for performing such experiments. Using recent examples, we examine how the labelled probes have been employed in combination with unlabelled ligands to quantify target engagement in cells and in animals. Finally, we consider future developments of this emerging methodology.

canSAR: update to the cancer translational research and drug discovery knowledgebase

canSAR (http://cansar.icr.ac.uk) is a public, freely available, integrative translational research and drug discovery knowlegebase. canSAR informs researchers to help solve key bottlenecks in cancer translation and drug discovery. It integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and unique, comprehensive and orthogonal 'druggability' assessments. canSAR is widely used internationally by academia and industry. Here we describe major enhancements to canSAR including new and expanded data. We also describe the first components of canSARblack-an advanced, responsive, multi-device compatible redesign of canSAR with a question-led interface.

How to Prepare a Compound Collection Prior to Virtual Screening

Virtual screening is a well-established technique that has proven to be successful in the identification of novel biologically active molecules, including drug repurposing. Whether for ligand-based or for structure-based virtual screening, a chemical collection needs to be properly processed prior to in silico evaluation. Here we describe our step-by-step procedure for handling very large collections (up to billions) of compounds prior to virtual screening.

Leveraging Human Genetics to Guide Cancer Drug Development

PURPOSE

The high attrition rate of cancer drug development programs is a barrier to realizing the promise of precision oncology. We have examined whether the genetic insights from genome-wide association studies of cancer can guide drug development and repurposing in oncology.

MATERIALS AND METHODS

Across 37 cancers, we identified 955 genetic risk variants from the National Human Genome Research Institute-European Bioinformatics Institute genome-wide association study catalog. We linked these variants to target genes using strategies that were based on linkage disequilibrium, DNA three-dimensional structure, and integration of predicted gene function and expression. With the use of the Informa Pharmaprojects database, we identified genes that are targets of unique drugs and assessed the level of enrichment that would be afforded by incorporation of genetic information in preclinical and phase II studies. For targets not under development, we implemented machine learning approaches to assess druggability.

RESULTS

For all preclinical targets incorporating genetic information, a 2.00-fold enrichment of a drug being successfully approved could be achieved (95% CI, 1.14- to 3.48-fold; P = .02). For phase II targets, a 2.75-fold enrichment could be achieved (95% CI, 1.42- to 5.35-fold; P < .001). Application of genetic information suggests potential repurposing of 15 approved nononcology drugs.

CONCLUSION

The findings illustrate the value of using insights from the genetics of inherited cancer susceptibility discovery projects as part of a data-driven strategy to inform drug discovery. Support for cancer germline genetic information for prospective targets is available online from the Institute of Cancer Research.

Probing the chemical-biological relationship space with the Drug Target Explorer

Modern phenotypic high-throughput screens (HTS) present several challenges including identifying the target(s) that mediate the effect seen in the screen, characterizing ‘hits’ with a polypharmacologic target profile, and contextualizing screen data within the large space of drugs and screening models. To address these challenges, we developed the Drug–Target Explorer. This tool allows users to query molecules within a database of experimentally-derived and curated compound-target interactions to identify structurally similar molecules and their targets. It enables network-based visualizations of the compound-target interaction space, and incorporates comparisons to publicly-available in vitro HTS datasets. Furthermore, users can identify molecules using a query target or set of targets. The Drug Target Explorer is a multifunctional platform for exploring chemical space as it relates to biological targets, and may be useful at several steps along the drug development pipeline including target discovery, structure–activity relationship, and lead compound identification studies.

New tools for carbohydrate sulfation analysis: heparan sulfate 2-O-sulfotransferase (HS2ST) is a target for small-molecule protein kinase inhibitors

Sulfation of carbohydrate residues occurs on a variety of glycans destined for secretion, and this modification is essential for efficient matrix-based signal transduction. Heparan sulfate (HS) glycosaminoglycans control physiological functions ranging from blood coagulation to cell proliferation. HS biosynthesis involves membrane-bound Golgi sulfotransferases, including HS 2-O-sulfotransferase (HS2ST), which transfers sulfate from the cofactor PAPS (3'-phosphoadenosine 5'-phosphosulfate) to the 2-O position of α-l-iduronate in the maturing polysaccharide chain. The current lack of simple non-radioactive enzyme assays that can be used to quantify the levels of carbohydrate sulfation hampers kinetic analysis of this process and the discovery of HS2ST inhibitors. In the present paper, we describe a new procedure for thermal shift analysis of purified HS2ST. Using this approach, we quantify HS2ST-catalysed oligosaccharide sulfation using a novel synthetic fluorescent substrate and screen the Published Kinase Inhibitor Set, to evaluate compounds that inhibit catalysis. We report the susceptibility of HS2ST to a variety of cell-permeable compounds in vitro, including polyanionic polar molecules, the protein kinase inhibitor rottlerin and oxindole-based RAF kinase inhibitors. In a related study, published back-to-back with the present study, we demonstrated that tyrosyl protein sulfotranferases are also inhibited by a variety of protein kinase inhibitors. We propose that appropriately validated small-molecule compounds could become new tools for rapid inhibition of glycan (and protein) sulfation in cells, and that protein kinase inhibitors might be repurposed or redesigned for the specific inhibition of HS2ST.