Unexplored therapeutic opportunities in the human genome

Targeting Orphan G Protein-Coupled Receptor 17 with T0 Ligand Impairs Glioblastoma Growth

Simple Summary

Glioblastoma multiforme (GBM), or glioblastoma chemotherapy, has one of the poorest improvements across all types of cancers. Despite the different rationales explored in targeted therapy for taming the GBM aggressiveness, its phenotypic plasticity, drug toxicity, and adaptive resistance mechanisms pose many challenges in finding an effective cure. Our manuscript reports the expression and prognostic role of orphan receptor GPR17 in glioma, the molecular mechanism of action of the novel ligand of GPR17, and provides evidence how the T0 agonist promotes glioblastoma cell death through modulation of the MAPK/ERK, PI3K–Akt, STAT, and NF-κB pathways. The highlights are as follows: GPR17 expression is associated with greater survival for both low-grade glioma (LGG) and GBM; GA-T0, a potent GPR17 receptor agonist, causes significant GBM cell death and apoptosis; GPR17 signaling promotes cell cycle arrest at the G1 phase in GBM cells; key genes are modulated in the signaling pathways that inhibit GBM cell proliferation; and GA-T0 crosses the blood–brain barrier and reduces tumor volume.

Abstract

Glioblastoma, an invasive high-grade brain cancer, exhibits numerous treatment challenges. Amongst the current therapies, targeting functional receptors and active signaling pathways were found to be a potential approach for treating GBM. We exploited the role of endogenous expression of GPR17, a G protein-coupled receptor (GPCR), with agonist GA-T0 in the survival and treatment of GBM. RNA sequencing was performed to understand the association of GPR17 expression with LGG and GBM. RT-PCR and immunoblotting were performed to confirm the endogenous expression of GPR17 mRNA and its encoded protein. Biological functions of GPR17 in the GBM cells was assessed by in vitro analysis. HPLC and histopathology in wild mice and an acute-toxicity analysis in a patient-derived xenograft model were performed to understand the clinical implication of GA-T0 targeting GPR17. We observed the upregulation of GPR17 in association with improved survival of LGG and GBM, confirming it as a predictive biomarker. GA-T0-stimulated GPR17 leads to the inhibition of cyclic AMP and calcium flux. GPR17 signaling activation enhances cytotoxicity against GBM cells and, in patient tissue-derived mesenchymal subtype GBM cells, induces apoptosis and prevents proliferation by stoppage of the cell cycle at the G1 phase. Modulation of the key genes involved in DNA damage, cell cycle arrest, and in several signaling pathways, including MAPK/ERK, PI3K–Akt, STAT, and NF-κB, prevents tumor regression. In vivo activation of GPR17 by GA-T0 reduces the tumor volume, uncovering the potential of GA-T0–GPR17 as a targeted therapy for GBM treatment. Conclusion: Our analysis suggests that GA-T0 targeting the GPR17 receptor presents a novel therapy for treating glioblastoma.

Innovations and Patent Trends in the Development of USFDA Approved Protein Kinase Inhibitors in the Last Two Decades

Protein kinase inhibitors (PKIs) are important therapeutic agents. As of 31 May 2021, the United States Food and Drug Administration (USFDA) has approved 70 PKIs. Most of the PKIs are employed to treat cancer and inflammatory diseases. Imatinib was the first PKI approved by USFDA in 2001. This review summarizes the compound patents and the essential polymorph patents of the PKIs approved by the USFDA from 2001 to 31 May 2021. The dates on the generic drug availability of the PKIs in the USA market have also been forecasted. It is expected that 19 and 48 PKIs will be genericized by 2025 and 2030, respectively, due to their compound patent expiry. This may reduce the financial toxicity associated with the existing PKIs. There are nearly 535 reported PKs. However, the USFDA approved PKIs target only about 10-15% of the total said PKs. As a result, there are still a large number of unexplored PKs. As the field advances during the next 20 years, one can anticipate that PKIs with many scaffolds, chemotypes, and pharmacophores will be developed.

A Computational Exploration of the Molecular Network Associated to Neuroinflammation in Alzheimer's Disease

Neuroinflammation, as defined by the presence of classically activated microglia, is thought to play a key role in numerous neurodegenerative disorders such as Alzheimer’s disease. While modulating neuroinflammation could prove beneficial against neurodegeneration, identifying its most relevant biological processes and pharmacological targets remains highly challenging. In the present study, we combined text-mining, functional enrichment and protein-level functional interaction analyses to 1) identify the proteins significantly associated to neuroinflammation in Alzheimer’s disease over the scientific literature, 2) distinguish the key proteins most likely to control the neuroinflammatory processes significantly associated to Alzheimer's disease, 3) identify their regulatory microRNAs among those dysregulated in Alzheimer's disease and 4) assess their pharmacological targetability. 94 proteins were found to be significantly associated to neuroinflammation in Alzheimer’s disease over the scientific literature and IL4, IL10 and IL13 signaling as well as TLR-mediated MyD88- and TRAF6-dependent responses were their most significantly enriched biological processes. IL10, TLR4, IL6, AKT1, CRP, IL4, CXCL8, TNF-alpha, ITGAM, CCL2 and NOS3 were identified as the most potent regulators of the functional interaction network formed by these immune processes. These key proteins were indexed to be regulated by 63 microRNAs dysregulated in Alzheimer's disease, 13 long non-coding RNAs and targetable by 55 small molecules and 8 protein-based therapeutics. In conclusion, our study identifies eleven key proteins with the highest ability to control neuroinflammatory processes significantly associated to Alzheimer’s disease, as well as pharmacological compounds with single or pleiotropic actions acting on them. As such, it may facilitate the prioritization of diagnostic and target-engagement biomarkers as well as the development of effective therapeutic strategies against neuroinflammation in Alzheimer’s disease.

How to Illuminate the Druggable Genome Using Pharos

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied "dark" targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Search for a target and view details Alternate Protocol: Search for dark target and view details Basic Protocol 2: Filter a target list to get refined results.

Recent advances in drug repurposing using machine learning

Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlight several applications for cancer using kinase inhibitors, Alzheimer's disease as well as COVID-19.

NEK5 activity regulates the mesenchymal and migratory phenotype in breast cancer cells

PURPOSE

Breast cancer remains a prominent global disease affecting women worldwide despite the emergence of novel therapeutic regimens. Metastasis is responsible for most cancer-related deaths, and acquisition of a mesenchymal and migratory cancer cell phenotypes contributes to this devastating disease. The utilization of kinase targets in drug discovery have revolutionized the field of cancer research but despite impressive advancements in kinase-targeting drugs, a large portion of the human kinome remains understudied in cancer. NEK5, a member of the Never-in-mitosis kinase family, is an example of such an understudied kinase. Here, we characterized the function of NEK5 in breast cancer.

METHODS

Stably overexpressing NEK5 cell lines (MCF7) and shRNA knockdown cell lines (MDA-MB-231, TU-BcX-4IC) were utilized. Cell morphology changes were evaluated using immunofluorescence and quantification of cytoskeletal components. Cell proliferation was assessed by Ki-67 staining and transwell migration assays tested cell migration capabilities. In vivo experiments with murine models were necessary to demonstrate NEK5 function in breast cancer tumor growth and metastasis.

RESULTS

NEK5 activation altered breast cancer cell morphology and promoted cell migration independent of effects on cell proliferation. NEK5 overexpression or knockdown does not alter tumor growth kinetics but promotes or suppresses metastatic potential in a cell type-specific manner, respectively.

CONCLUSION

While NEK5 activity modulated cytoskeletal changes and cell motility, NEK5 activity affected cell seeding capabilities but not metastatic colonization or proliferation in vivo. Here we characterized NEK5 function in breast cancer systems and we implicate NEK5 in regulating specific steps of metastatic progression.

In vivo synthetic chemistry of proteolysis targeting chimeras (PROTACs)

Chemical knockdown of therapeutic targets using proteolysis targeting chimeras (PROTACs) is a rapidly developing field in drug discovery, but PROTACs are bifunctional molecules that generally show poor bioavailability due to their relatively high molecular weight. Recent developments aimed at the development of next-generation PROTACs include the in vivo synthesis of PROTAC molecules, and the exploitation of PROTACs as chemical tools for in vivo synthesis of ubiquitinated proteins. This short review covers recent advances in these areas and discusses the prospects for in vivo synthetic PROTAC technology.

Bioactivity descriptors for uncharacterized chemical compounds

Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks.

Human GPR17 missense variants identified in metabolic disease patients have distinct downstream signaling profiles

GPR17 is a G-protein-coupled receptor (GPCR) implicated in the regulation of glucose metabolism and energy homeostasis. Such evidence is primarily drawn from mouse knockout studies and suggests GPR17 as a potential novel therapeutic target for the treatment of metabolic diseases. However, links between human GPR17 genetic variants, downstream cellular signaling, and metabolic diseases have yet to be reported. Here, we analyzed GPR17 coding sequences from control and disease cohorts consisting of individuals with adverse clinical metabolic deficits including severe insulin resistance, hypercholesterolemia, and obesity. We identified 18 nonsynonymous GPR17 variants, including eight variants that were exclusive to the disease cohort. We characterized the protein expression levels, membrane localization, and downstream signaling profiles of nine GPR17 variants (F43L, V96M, V103M, D105N, A131T, G136S, R248Q, R301H, and G354V). These nine GPR17 variants had similar protein expression and subcellular localization as wild-type GPR17; however, they showed diverse downstream signaling profiles. GPR17-G136S lost the capacity for agonist-mediated cAMP, Ca²⁺, and β-arrestin signaling. GPR17-V96M retained cAMP inhibition similar to GPR17-WT, but showed impaired Ca²⁺ and β-arrestin signaling. GPR17-D105N displayed impaired cAMP and Ca²⁺ signaling, but unaffected agonist-stimulated β-arrestin recruitment. The identification and functional profiling of naturally occurring human GPR17 variants from individuals with metabolic diseases revealed receptor variants with diverse signaling profiles, including differential signaling perturbations that resulted in GPCR signaling bias. Our findings provide a framework for structure–function relationship studies of GPR17 signaling and metabolic disease.

Target-Based Evaluation of "Drug-Like" Properties and Ligand Efficiencies

Physicochemical descriptors commonly used to define "drug-likeness" and ligand efficiency measures are assessed for their ability to differentiate marketed drugs from compounds reported to bind to their efficacious target or targets. Using ChEMBL version 26, a data set of 643 drugs acting on 271 targets was assembled, comprising 1104 drug-target pairs having ≥100 published compounds per target. Taking into account changes in their physicochemical properties over time, drugs are analyzed according to their target class, therapy area, and route of administration. Recent drugs, approved in 2010-2020, display no overall differences in molecular weight, lipophilicity, hydrogen bonding, or polar surface area from their target comparator compounds. Drugs are differentiated from target comparators by higher potency, ligand efficiency (LE), lipophilic ligand efficiency (LLE), and lower carboaromaticity. Overall, 96% of drugs have LE or LLE values, or both, greater than the median values of their target comparator compounds.

Advances in mouse genetics for the study of human disease

The mouse is the pre-eminent model organism for studies of mammalian gene function and has provided an extraordinarily rich range of insights into basic genetic mechanisms and biological systems. Over several decades, the characterization of mouse mutants has illuminated the relationship between gene and phenotype, providing transformational insights into the genetic bases of disease. However, if we are to deliver the promise of genomic and precision medicine, we must develop a comprehensive catalogue of mammalian gene function that uncovers the dark genome and elucidates pleiotropy. Advances in large-scale mouse mutagenesis programmes allied to high-throughput mouse phenomics are now addressing this challenge and systematically revealing novel gene function and multi-morbidities. Alongside the development of these pan-genomic mutational resources, mouse genetics is employing a range of diversity resources to delineate gene–gene and gene–environment interactions and to explore genetic context. Critically, mouse genetics is a powerful tool for assessing the functional impact of human genetic variation and determining the causal relationship between variant and disease. Together these approaches provide unique opportunities to dissect in vivo mechanisms and systems to understand pathophysiology and disease. Moreover, the provision and utility of mouse models of disease has flourished and engages cumulatively at numerous points across the translational spectrum from basic mechanistic studies to pre-clinical studies, target discovery and therapeutic development.

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Mass spectrometry informs the structure and dynamics of membrane proteins involved in lipid and drug transport

Membrane proteins are important macromolecules that play crucial roles in many cellular and physiological processes. Over the past two decades, the use of mass spectrometry as a biophysical tool to characterise membrane proteins has grown steadily. By capturing these dynamic complexes in the gas phase, many unknown small molecule interactions have been revealed. One particular application of this research has been the focus on antibiotic resistance with considerable efforts being made to understand underlying mechanisms. Here we review recent advances in the application of mass spectrometry that have yielded both structural and dynamic information on the interactions of antibiotics with proteins involved in bacterial cell envelope biogenesis and drug efflux.

An Approach for the Real-Time Quantification of Cytosolic Protein-Protein Interactions in Living Cells

In recent years, cell-based assays have been frequently used in molecular interaction analysis. Cell-based assays complement traditional biochemical and biophysical methods, as they allow for molecular interaction analysis, mode of action studies, and even drug screening processes to be performed under physiologically relevant conditions. In most cellular assays, biomolecules are usually labeled to achieve specificity. In order to overcome some of the drawbacks associated with label-based assays, we have recently introduced "cell-based molography" as a biosensor for the analysis of specific molecular interactions involving native membrane receptors in living cells. Here, we expand this assay to cytosolic protein-protein interactions. First, we created a biomimetic membrane receptor by tethering one cytosolic interaction partner to the plasma membrane. The artificial construct is then coherently arranged into a two-dimensional pattern within the cytosol of living cells. Thanks to the molographic sensor, the specific interactions between the coherently arranged protein and its endogenous interaction partners become visible in real time without the use of a fluorescent label. This method turns out to be an important extension of cell-based molography because it expands the range of interactions that can be analyzed by molography to those in the cytosol of living cells.

Knowledge graphs and their applications in drug discovery

INTRODUCTION

Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems biology approach, knowledge graphs have emerged as attractive methods of data storage and hypothesis generation.

AREAS COVERED

In this review, the author summarizes the applications of knowledge graphs in drug discovery. They evaluate their utility; differentiating between academic exercises in graph theory, and useful tools to derive novel insights, highlighting target identification and drug repurposing as two areas showing particular promise. They provide a case study on COVID-19, summarizing the research that used knowledge graphs to identify repurposable drug candidates. They describe the dangers of degree and literature bias, and discuss mitigation strategies.

EXPERT OPINION

Whilst knowledge graphs and graph-based machine learning have certainly shown promise, they remain relatively immature technologies. Many popular link prediction algorithms fail to address strong biases in biomedical data, and only highlight biological associations, failing to model causal relationships in complex dynamic biological systems. These problems need to be addressed before knowledge graphs reach their true potential in drug discovery.

Development and biological applications of sulfur-triazole exchange (SuTEx) chemistry

Sulfur electrophiles constitute an important class of covalent small molecules that have found widespread applications in synthetic chemistry and chemical biology. Various electrophilic scaffolds, including sulfonyl fluorides and arylfluorosulfates as recent examples, have been applied for protein bioconjugation to probe ligand sites amenable for chemical proteomics and drug discovery. In this review, we describe the development of sulfonyl-triazoles as a new class of electrophiles for sulfur-triazole exchange (SuTEx) chemistry. SuTEx achieves covalent reaction with protein sites through irreversible modification of a residue with an adduct group (AG) upon departure of a leaving group (LG). A principal differentiator of SuTEx from other chemotypes is the selection of a triazole heterocycle as the LG, which introduces additional capabilities for tuning the sulfur electrophile. We describe the opportunities afforded by modifications to the LG and AG alone or in tandem to facilitate nucleophilic substitution reactions at the SO2 center in cell lysates and live cells. As a result of these features, SuTEx serves as an efficient platform for developing chemical probes with tunable bioactivity to study novel nucleophilic sites on established and poorly annotated protein targets. Here, we highlight a suite of biological applications for the SuTEx electrophile and discuss future goals for this enabling covalent chemistry.

Epigenetic Target Fishing with Accurate Machine Learning Models

Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.

Protein network exploration prioritizes targets for modulating neuroinflammation in Parkinson's disease

Parkinson's disease is a progressive neurodegenerative disease associated with a loss of dopaminergic neurons in the substantia nigra of the brain. Neuroinflammation, another hallmark of the disease, is thought to play an important role in the neurodegenerative process. While mitigating neuroinflammation could prove beneficial for Parkinson's disease, identifying the most relevant biological processes and pharmacological targets as well as drugs to modulate them remains highly challenging. The present study aimed to better understand the protein network behind neuroinflammation in Parkinson's disease and to prioritize possible targets for its pharmacological modulation. We first used text-mining to systematically collect the proteins significantly associated to Parkinson's disease neuroinflammation over the scientific literature. The functional interaction network formed by these proteins was then analyzed by integrating functional enrichment, network topology analysis and drug-protein interaction analysis. We identified 57 proteins significantly associated to neuroinflammation in Parkinson's disease. Toll-like Receptor Cascades as well as Interleukin 4, Interleukin 10 and Interleukin 13 signaling appeared as the most significantly enriched biological processes. Protein network analysis using STRING and CentiScaPe identified 8 proteins with the highest ability to control these biological processes underlying neuroinflammation, namely caspase 1, heme oxygenase 1, interleukin 1beta, interleukin 4, interleukin 6, interleukin 10, tumor necrosis factor alpha and toll-like receptor 4. These key proteins were indexed to be targetable by a total of 38 drugs including 27 small compounds 11 protein-based therapies. In conclusion, our study highlights key proteins in Parkinson's disease neuroinflammation as well as pharmacological compounds acting on them. As such, it may facilitate the prioritization of biomarkers for the development of diagnostic, target-engagement assessment and therapeutic tools against Parkinson's disease.

Genetic association and causal inference converge on hyperglycaemia as a modifiable factor to improve lung function

Measures of lung function are heritable, and thus, we sought to utilise genetics to propose drug-repurposing candidates that could improve respiratory outcomes. Lung function measures were found to be genetically correlated with seven druggable biochemical traits, with further evidence of a causal relationship between increased fasting glucose and diminished lung function. Moreover, we developed polygenic scores for lung function specifically within pathways with known drug targets and investigated their relationship with pulmonary phenotypes and gene expression in independent cohorts to prioritise individuals who may benefit from particular drug-repurposing opportunities. A transcriptome-wide association study (TWAS) of lung function was then performed which identified several drug–gene interactions with predicted lung function increasing modes of action. Drugs that regulate blood glucose were uncovered through both polygenic scoring and TWAS methodologies. In summary, we provided genetic justification for a number of novel drug-repurposing opportunities that could improve lung function.

Chronic respiratory disorders like asthma affect around 600 million people worldwide. Although these illnesses are widespread, they can have several different underlying causes, making them difficult to treat. Drugs that work well on one type of respiratory disorder may be completely ineffective on another. Understanding the biological and environmental factors that cause these illnesses will allow them to be treated more effectively by tailoring therapies to each patient.

Reduced lung function is a factor in respiratory disorders and it can have many genetic causes. Studying the genes of patients with reduced lung function can reveal the genes involved, some of which may already be targets of existing drugs for other illnesses. So, could a patient’s genetics be used to repurpose existing drugs to treat their respiratory disorders?

Reay et al. combined three methods to link genetics and biological processes to the causes of reduced lung function. The results reveal several factors that could lead to new treatments. In one example, reduced lung function showed a link to genes associated with high blood sugar. As such, treatments used in diabetes might help improve lung function in some patients. Reay et al. also developed a scoring system that could predict the efficacy of a treatment based on a patient’s genetics. The study suggests that COVID-19 infection could be affected by blood sugar levels too.

Chronic respiratory disorders are a critical issue worldwide and have proven difficult to treat, but these results suggest a way to identify new therapies and target them to the right patients. The findings also support a connection between lung function and blood sugar levels. This implies that perhaps existing diabetes treatments – including diet and lifestyle changes aimed at reducing or limiting blood sugar – could be repurposed to treat respiratory disorders in some patients. The next step will be to perform clinical trials to test whether these therapies are in fact effective.

Pan-Cancer Analysis of Human Kinome Gene Expression and Promoter DNA Methylation Identifies Dark Kinase Biomarkers in Multiple Cancers

Kinases are a group of intracellular signaling molecules that play critical roles in various biological processes. Even though kinases comprise one of the most well-known therapeutic targets, many have been understudied and therefore warrant further investigation. DNA methylation is one of the key epigenetic regulators that modulate gene expression. In this study, the human kinome's DNA methylation and gene expression patterns were analyzed using the level-3 TCGA data for 32 cancers. Unsupervised clustering based on kinome data revealed the grouping of cancers based on their organ level and tissue type. We further observed significant differences in overall kinase methylation levels (hyper- and hypomethylation) between the tumor and adjacent normal samples from the same tissue. Methylation expression quantitative trait loci (meQTL) analysis using kinase gene expression with the corresponding methylated probes revealed a highly significant and mostly negative association (~92%) within 1.5 kb from the transcription start site (TSS). Several understudied (dark) kinases (PKMYT1, PNCK, BRSK2, ERN2, STK31, STK32A, and MAPK4) were also identified with a significant role in patient survival. This study leverages results from multi-omics data to identify potential kinase markers of prognostic and diagnostic importance and further our understanding of kinases in cancer.

Druggable genome and precision medicine in cancer: current challenges

The past decades have seen tremendous developments with respect to "specific" therapeutics that target key signaling molecules to conquer cancer. The key advancements with multiomics technologies, especially genomics, have allowed physicians and molecular oncologists to design "tailor-made" solutions to the specific oncogenes that are deregulated in individual patients, a strategy which has turned out to be successful though the patients quickly develop resistance. The swift integration of multidisciplinary approaches has led to the development of "next generation" therapeutics and, with synergistic therapeutic regimes combined with immune checkpoint inhibitors to reactivate the dampened immune response, has provided the much-needed promise for cancer patients. Despite these advances, a large portion of the druggable genome remains understudied, and the role of druggable genome in the immune system needs further attention. Establishment of patient-derived organoid models has fastened the preclinical validation of novel therapeutics for swift clinical translation. We summarized the current advances and challenges and also stress the importance of biobanking and collection of longitudinal data sets with structured clinical information, as well as the critical role these "high content data sets" will play in designing new therapeutic regimes in a tailor-made fashion.

Prioritizing Pain-Associated Targets with Machine Learning

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.

The Playbooks of Medicinal Chemistry Design Moves

Large databases of biologically relevant molecules, such as ChEMBL, SureChEMBL, or compound collections of pharmaceutical or agrochemical companies, are invaluable sources of medicinal chemistry information, albeit implicit. We developed a modified matched molecular pair approach to systematically and exhaustively extract the transformations in these databases and distill them into snippets of explicit design knowledge that are easily interpretable and directly applicable. The resulting "playbooks of medicinal chemistry design moves" capture the collective pharmaceutical and agrochemical research expertise across multiple chemists, companies, targets, and projects. They can be queried in an automated fashion for systematic prospective design and compound generation. The ChEMBL playbook and an application to exploit it are available at https://github.com/mahendra-awale/medchem_moves.

Current and Future Challenges in Modern Drug Discovery

Drug discovery is an expensive, time-consuming, and risky business. To avoid late-stage failure, learnings from past projects and the development of new approaches are crucial. New modalities and emerging new target spaces allow the exploration of unprecedented indications or to address so far undrugable targets. Late-stage attrition is usually attributed to the lack of efficacy or to compound-related safety issues. Efficacy has been shown to be related to a strong genetic link to human disease, a better understanding of the target biology, and the availability of biomarkers to bridge from animals to humans. Compound safety can be improved by ligand optimization, which is becoming increasingly demanding for difficult targets. Therefore, new strategies include the design of allosteric ligands, covalent binders, and other modalities. Design methods currently heavily rely on artificial intelligence and advanced computational methods such as free energy calculations and quantum chemistry. Especially for quantum chemical methods, a more detailed overview is given in this chapter.

The Dark Kinase Knowledgebase: an online compendium of knowledge and experimental results of understudied kinases

Kinases form the backbone of numerous cell signaling pathways, with their dysfunction similarly implicated in multiple pathologies. Further facilitated by their druggability, kinases are a major focus of therapeutic development efforts in diseases such as cancer, infectious disease and autoimmune disorders. While their importance is clear, the role or biological function of nearly one-third of kinases is largely unknown. Here, we describe a data resource, the Dark Kinase Knowledgebase (DKK; https://darkkinome.org), that is specifically focused on providing data and reagents for these understudied kinases to the broader research community. Supported through NIH’s Illuminating the Druggable Genome (IDG) Program, the DKK is focused on data and knowledge generation for 162 poorly studied or ‘dark’ kinases. Types of data provided through the DKK include parallel reaction monitoring (PRM) peptides for quantitative proteomics, protein interactions, NanoBRET reagents, and kinase-specific compounds. Higher-level data is similarly being generated and consolidated such as tissue gene expression profiles and, longer-term, functional relationships derived through perturbation studies. Associated web tools that help investigators interrogate both internal and external data are also provided through the site. As an evolving resource, the DKK seeks to continually support and enhance knowledge on these potentially high-impact druggable targets.

Multi-omics analyses of cognitive traits and psychiatric disorders highlights brain-dependent mechanisms

Integrating findings from genome-wide association studies with molecular datasets can develop insight into the underlying functional mechanisms responsible for trait-associated genetic variants. We have applied the principles of Mendelian randomization (MR) to investigate whether brain-derived gene expression (n = 1194) may be responsible for mediating the effect of genetic variants on eight cognitive and psychological outcomes (attention deficit hyperactivity disorder (ADHD), Alzheimer's disease, bipolar disorder, depression, intelligence, insomnia, neuroticism and schizophrenia). Transcriptome-wide analyses identified 83 genes associated with at least one outcome (PBonferroni < 6.72 × 10-6), with multiple-trait colocalization also implicating changes to brain-derived DNA methylation at nine of these loci. Comparing effects between outcomes identified evidence of enrichment which may reflect putative causal relationships, such as an inverse relationship between genetic liability towards schizophrenia risk and cognitive ability in later life. Repeating these analyses in whole blood (n = 31 684), we replicated 58.2% of brain-derived effects (based on P < 0.05). Finally, we undertook phenome-wide evaluations at associated loci to investigate pleiotropic effects with 700 complex traits. This highlighted pleiotropic loci such as FURIN (initially implicated in schizophrenia risk (P = 1.05 × 10-7)) which had evidence of an effect on 28 other outcomes, as well as genes which may have a more specific role in disease pathogenesis (e.g. SLC12A5 which only provided evidence of an effect on depression (P = 7.13 × 10-10)). Our results support the utility of whole blood as a valuable proxy for informing initial target identification but also suggest that gene discovery in a tissue-specific manner may be more informative. Finally, non-pleiotropic loci highlighted by our study may be of use for therapeutic translational endeavours.

DrugCentral 2021 supports drug discovery and repositioning

DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for ∼1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the 'drugs in news' feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download from the DrugCentral web portal.

G protein-coupled receptors: structure- and function-based drug discovery

As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure-function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.

TCRD and Pharos 2021: mining the human proteome for disease biology

In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.

Drug design targeting active posttranslational modification protein isoforms

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.

COVID-19 research risks ignoring important host genes due to pre-established research patterns

It is known that research into human genes is heavily skewed towards genes that have been widely studied for decades, including many genes that were being studied before the productive phase of the Human Genome Project. This means that the genes most frequently investigated by the research community tend to be only marginally more important to human physiology and disease than a random selection of genes. Based on an analysis of 10,395 research publications about SARS-CoV-2 that mention at least one human gene, we report here that the COVID-19 literature up to mid-October 2020 follows a similar pattern. This means that a large number of host genes that have been implicated in SARS-CoV-2 infection by four genome-wide studies remain unstudied. While quantifying the consequences of this neglect is not possible, they could be significant.

Moving targets in drug discovery

Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing importance of integrative data-driven approaches. In this paper, data mining and integration is leveraged to inspect target innovation trends in drug discovery. The study highlights protein families and classes that have received more attention and those that have just emerged in the scientific literature, thus highlighting novel opportunities for drug intervention. In order to delineate the evolution of target-driven research interest from a biological perspective, trends in biological process annotations from Gene Ontology and disease annotations from DisGeNET are captured. The analysis reveals an increasing interest in targets related to immune system processes, and a recurrent trend for targets involved in circulatory system processes. At the level of diseases, targets associated with cancer-related pathologies, intellectual disability, and schizophrenia are increasingly investigated in recent years. The methodology enables researchers to capture trends in research attention in target space at an early stage during the drug discovery process. Workflows, scripts, and data used in this study are publicly available from https://github.com/BZdrazil/Moving_Targets. An interactive web application allows the customized exploration of target, biological process, and disease trends (available at https://rguha.shinyapps.io/MovingTargets/).

PROTACs to address the challenges facing small molecule inhibitors

Small molecule inhibitors of proteins represent important medicines and critical chemical tools to investigate the biology of the target proteins. Advances in various -omics technologies have fueled the pace of discovery of disease-relevant proteins. Translating these discoveries into human benefits requires us to develop specific chemicals to inhibit the proteins. However, traditional small molecule inhibitors binding to orthosteric or allosteric sites face significant challenges. These challenges include drug selectivity, therapy resistance as well as drugging undruggable proteins and multi-domain proteins. To address these challenges, PROteolysis TArgeting Chimera (PROTAC) has been proposed. PROTACs are heterobifunctional molecules containing a binding ligand for a protein of interest and E3 ligase-recruiting ligand that are connected through a chemical linker. Binding of a PROTAC to its target protein will bring a E3 ligase in close proximity to initiate polyubiquitination of the target protein ensuing its proteasome-mediated degradation. Unlike small molecule inhibitors, PROTACs achieve target protein degradation in its entirety in a catalytical fashion. In this review, we analyze recent advances in PROTAC design to discuss how PROTACs can address the challenges facing small molecule inhibitors to potentially deliver next-generation medicines and chemical tools with high selectivity and efficacy. We also offer our perspectives on the future promise and potential limitations facing PROTACs. Investigations to overcome these limitations of PROTACs will further help realize the promise of PROTACs for human benefits.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

MOTIVATION

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.

RESULTS

Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

AVAILABILITY

https://neo4covid19.ncats.io.

Redirecting cytotoxic T cells with chemically programmed antibodies

T-cell engaging bispecific antibodies (T-biAbs) mediate potent and selective cytotoxicity by combining specificities for target and effector cells in one molecule. Chemically programmed T-biAbs (cp-T-biAbs) are precisely assembled compositions of (i) small molecules that govern cancer cell surface targeting with high affinity and specificity and (ii) antibodies that recruit and activate T cells and equip the small molecule with confined biodistribution and longer circulatory half-life. Conceptually similar to cp-T-biAbs, switchable chimeric antigen receptor T cells (sCAR-Ts) can also be put under the control of small molecules by using a chemically programmed antibody as a bispecific adaptor molecule. As such, cp-T-biAbs and cp-sCAR-Ts can endow small molecules with the power of cancer immunotherapy. We here review the concept of chemically programmed antibodies for recruiting and activating T cells as a promising strategy for broadening the utility of small molecules in cancer therapy.

Organic Composomes as Supramolecular Aptamers

Information contained in the sequences of biological polymers such as DNA and protein is crucial to determining their function. Lipids are not generally thought of as information-containing molecules. However, from a supramolecular perspective, the number of possible combinations of lipids in a mixture is comparable to the complexity of DNA or proteins. Here, we test the idea that an organic composome can exhibit molecular recognition. We use water/octanol as a model two-phase system and investigate the effect of organic solutes in different combinations in the organic phase on selective partitioning of two water-soluble dyes (Brilliant Blue FCF and Allura Red AC) from the aqueous phase into the organic phase. We found that variation in the concentration of the surfactant cetyltrimethylamonium bromide (CTAB) in the octanol phase alone was sufficient to cause a switch in selectivity, with low CTAB concentrations being selective for the red dye and high CTAB concentrations being selective for the blue dye. Other organic components were added to the organic phase to introduce molecular diversity into the composome and directed evolution was used to optimize the relative concentrations of the solutes. An improvement of selective partitioning in the heterogeneous system over the pure CTAB solution was observed. The results indicate that supramolecular composomes are sufficient for molecular recognition processes in a way analogous to nucleic acid aptamers.

Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs

Incorrect drug target identification is a major obstacle in drug discovery. Only 15% of drugs advance from Phase II to approval, with ineffective targets accounting for over 50% of these failures^1–3. Advances in data fusion and computational modeling have independently progressed towards addressing this issue. Here, we capitalize on both these approaches with Rosalind, a comprehensive gene prioritization method that combines heterogeneous knowledge graph construction with relational inference via tensor factorization to accurately predict disease-gene links. Rosalind demonstrates an increase in performance of 18%-50% over five comparable state-of-the-art algorithms. On historical data, Rosalind prospectively identifies 1 in 4 therapeutic relationships eventually proven true. Beyond efficacy, Rosalind is able to accurately predict clinical trial successes (75% recall at rank 200) and distinguish likely failures (74% recall at rank 200). Lastly, Rosalind predictions were experimentally tested in a patient-derived in-vitro assay for Rheumatoid arthritis (RA), which yielded 5 promising genes, one of which is unexplored in RA.

Crystal Structure and Inhibitor Identifications Reveal Targeting Opportunity for the Atypical MAPK Kinase ERK3

Extracellular signal-regulated kinase 3 (ERK3), known also as mitogen-activated protein kinase 6 (MAPK6), is an atypical member of MAPK kinase family, which has been poorly studied. Little is known regarding its function in biological processes, yet this atypical kinase has been suggested to play important roles in the migration and invasiveness of certain cancers. The lack of tools, such as a selective inhibitor, hampers the study of ERK3 biology. Here, we report the crystal structure of the kinase domain of this atypical MAPK kinase, providing molecular insights into its distinct ATP binding pocket compared to the classical MAPK ERK2, explaining differences in their inhibitor binding properties. Medium-scale small molecule screening identified a number of inhibitors, several of which unexpectedly exhibited remarkably high inhibitory potencies. The crystal structure of CLK1 in complex with CAF052, one of the most potent inhibitors identified for ERK3, revealed typical type-I binding mode of the inhibitor, which by structural comparison could likely be maintained in ERK3. Together with the presented structural insights, these diverse chemical scaffolds displaying both reversible and irreversible modes of action, will serve as a starting point for the development of selective inhibitors for ERK3, which will be beneficial for elucidating the important functions of this understudied kinase.

Recent progress on cheminformatics approaches to epigenetic drug discovery

The ability of epigenetic markers to affect genome function has enabled transformative changes in drug discovery, especially in cancer and other emerging therapeutic areas. Concordant with the introduction of the term 'epi-informatics', the size of the epigenetically relevant chemical space has grown substantially and so did the number of applications of cheminformatic methods to epigenetics. Recent progress in epi-informatics has improved our understanding of the structure-epigenetic activity relationships and boosted the development of models predicting novel epigenetic agents. Herein, we review the advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles, summarize the current chemogenomics data available for epigenetic targets, and provide a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery.

ZMAT2 in Humans and Other Primates: A Highly Conserved and Understudied Gene

Recent advances in genetics present unique opportunities for enhancing our understanding of human physiology and disease predisposition through detailed analysis of gene structure, expression, and population variation via examination of data in publicly accessible genome and gene expression repositories. Yet, the vast majority of human genes remain understudied. Here, we show the scope of these genomic and genetic resources by evaluating ZMAT2, a member of a 5-gene family that through May 2020 had been the focus of only 4 peer-reviewed scientific publications. Using analysis of information extracted from public databases, we show that human ZMAT2 is a 6-exon gene and find that it exhibits minimal genetic variation in human populations and in disease states, including cancer. We further demonstrate that the gene and its encoded protein are highly conserved among nonhuman primates and define a cohort of ZMAT2 pseudogenes in the marmoset genome. Collectively, our investigations illustrate how complementary use of genomic, gene expression, and population genetic resources can lead to new insights about human and mammalian biology and evolution, and when coupled with data supporting key roles for ZMAT2 in keratinocyte differentiation and pre-RNA splicing argue that this gene is worthy of further study.

Soluble ligands as drug targets

Historically, the main classes of drug targets have been receptors, enzymes, ion channels and transporters. However, owing largely to the rise of antibody-based therapies in the past two decades, soluble protein ligands such as inflammatory cytokines have become an increasingly important class of drug targets. In this Review, we analyse drugs targeting ligands that have reached clinical development at some point since 1992. We identify 291 drugs that target 99 unique ligands, and we discuss trends in the characteristics of the ligands, drugs and indications for which they have been tested. In the last 5 years, the number of ligand-targeting drugs approved by the FDA has doubled to 34, while the number of clinically validated ligand targets has doubled to 22. Cytokines and growth factors are the predominant types of targeted ligands (70%), and inflammation and autoimmune disorders, cancer and ophthalmological diseases are the top therapeutic areas for both approved agents and agents in clinical studies, reflecting the central role of cytokine and/or growth factor pathways in such diseases.

Leveraging genetic data to investigate molecular targets and drug repurposing candidates for treating alcohol use disorder and hepatotoxicity

BACKGROUND

Novel treatments for alcohol use disorder (AUD) and alcohol-related liver disease (ALD) are greatly needed. Genetic information can improve drug discovery rates by facilitating the identification of novel biological targets and potential drugs for repurposing.

METHODS

The present study utilized a recently developed Bayesian approach, Integrative Risk Gene Selector (iRIGS), to identify additional risk genes for alcohol consumption using SNPs from the largest alcohol consumption GWAS to date (N = 941,280). iRIGS incorporates several genomic features and closeness of these genes in network space to compute a posterior probability for protein coding genes near each SNP. We subsequently used the Target Central Resource Database to search for drug-protein interactions for these newly identified genes and previously identified risk genes for alcohol consumption.

RESULTS

We identified several genes that are novel contributions to the previously published alcohol consumption GWAS. Namely, ACVR2A, which is critical for liver function and linked to anxiety and cocaine self-administration, and PRKCE, which has been linked to alcohol self-administration. Notably, only a minority of the SNPs (18.4 %) were linked to genes with confidence (>0.75), underscoring the need to apply multiple methods to assign function to loci. Finally, some previously identified risk genes for alcohol consumption code for proteins that are implicated in liver function and are targeted by drugs, some of which are candidates for managing hepatotoxicity.

CONCLUSIONS

This study demonstrates the value of incorporating regulatory information and drug-protein interaction data to highlight additional molecular targets and drug repurposing candidates for treating AUD and ALD.

Activity- and reactivity-based proteomics: Recent technological advances and applications in drug discovery

Activity-based protein profiling (ABPP) is recognized as a powerful and versatile chemoproteomic technology in drug discovery. Central to ABPP is the use of activity-based probes to report the activity of specific enzymes or reactivity of amino acid types in complex biological systems. Over the last two decades, ABPP has facilitated the identification of new drug targets and discovery of lead compounds in human and infectious disease. Furthermore, as part of a sustained global effort to illuminate the druggable proteome, the repertoire of target classes addressable with activity-based probes has vastly expanded in recent years. Here, we provide an overview of ABPP and summarise the major technological advances with an emphasis on probe development.