The NCGC pharmaceutical collection: a comprehensive resource of clinically approved drugs enabling repurposing and chemical genomics

Application of cytochrome P450 enzyme assays to predict p53 inducers and AChE inhibitors that require metabolic activation

Metabolically active compounds can cause toxicity which would otherwise be undetected using traditional in vitro assays with limited proficiency for xenobiotic metabolism. Introduction of liver microsomes to assay systems enables enhanced identification of compounds that require biotransformation to induce toxicity. Previously, metabolically active compounds from the Tox21 10 K compound library were identified using assays probing two targets, p53 and acetylcholinesterase (AChE), in the presence and absence of human or rat liver microsomes, due to the established roles of cytochrome P450 (CYP) enzymes in human drug metabolism. To further explore the role of metabolic activation, the activities of the identified metabolically active compounds were evaluated against five CYP enzymes: CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4. CYP bioactivities were found to be highly predictive (>80 % accuracy) of compounds that required metabolic activation in these assays. Chemical features significantly enriched in metabolically active compounds, as well as chemical features that were specific for each of the five CYPs, were identified. Product use exposures of the metabolically active compounds were examined in this study, with "pesticides" appearing to be the largest category that may produce harmful metabolites. Additionally, the compound interactions with different CYPs were assessed and frequencies for both classes of compounds, drugs and environmental chemicals, were found to be proportionally similar across the five CYP isoforms.

High-throughput screening identifies Aurora kinase B as a critical therapeutic target for Merkel cell carcinoma

Merkel cell carcinoma (MCC) is a rare, aggressive skin cancer. Most MCCs contain Merkel cell polyomavirus (virus-positive MCC; VP-MCC), and the remaining are virus-negative (VN-MCC). Immune checkpoint inhibitors are the first-line treatment for metastatic MCC, but durable responses are achieved in less than 50% of patients. To identify new treatments, we screen ~4,000 compounds for their ability to reduce MCC viability and demonstrate that VP-MCC and VN-MCC exhibit distinct response profiles. Aurora kinase inhibitors selectively reduce VP-MCC viability, with RNAi screening independently identifying AURKB as an essential gene for MCC survival, especially in VP-MCC. AZD2811, a selective AURKB inhibitor, induces mitotic dysregulation and apoptosis in MCC cells, with greater efficacy in VP-MCC. In mice, AZD2811 nanoparticles inhibit tumor growth and increase survival in both VP-MCC and VN-MCC xenograft models. Overall, our unbiased screens identify AURKB as a promising therapeutic target and AZD2811NP as a potential treatment for MCC.

Pan-genome analysis and drug repurposing strategies for extensively drug-resistant Salmonella Typhi: Subtractive genomics and e-pharmacophore approaches

In the current study, we presented the genome sequence and taxonomic classification of the new extensively drug-resistant (XDR) Salmonella enterica serovar Typhi strain JRCGR-ST-AK02. Its genome size was found to be 4,780,534 bp, containing 4864 genes. Taxonomic classification was performed based on the Average Nucleotide Identity (ANI), Genome-to-Genome Distance Calculator (GGDC) and Average Amino Acid Identity (AAI) analysis. Pan-genome analysis revealed 34,4915 core genes, which are predominantly involved in general functions and carbohydrate metabolism. We used a subtractive genomics approach and identified the PocR protein as a drug target. Its 3D structure was built using homology modeling, and an e-pharmacophore hypothesis was created using its binding site. The pharmacophore hypothesis was screened against FDA-approved ligands library and a total of 2018 out 9392 drugs were selected for molecular docking. Cangrelor and Pentagastrin presented docking scores of -9.503 and -9.081 kcal/mol, respectively. The binding dynamics of these promising FDA-approved drugs were further confirmed through 200 ns molecular dynamics simulation, highlighting their stable and strong interactions with the PocR protein. Our study highlights the potential of Cangrelor and Pentagastrin for repurposing against XDR Salmonella Typhi. By identifying these drugs as promising candidates, we pave the way for new treatments for XDR Salmonella Typhi infections.

Proteasome Inhibitors Induce Apoptosis in Ex Vivo Cells of T-Cell Prolymphocytic Leukemia

Finding an effective treatment for T-PLL patients remains a significant challenge. Alemtuzumab, currently the gold standard, is insufficient in managing the aggressiveness of the disease in the long term. Consequently, numerous efforts are underway to address this unmet clinical need. The rarity of the disease limits the ability to conduct robust clinical trials, making in silico, ex vivo, and in vivo drug screenings essential for designing new therapeutic strategies for T-PLL. We conducted a drug repurposing analysis based on T-PLL gene expression data and identified proteasome inhibitors (PIs) as a promising new class of compounds capable of reversing the T-PLL phenotype. Treatment of ex vivo T-PLL cells with Bortezomib and Carfilzomib, two PI compounds, supported this hypothesis by demonstrating increased apoptosis in leukemic cells. The current lack of a suitable in vitro model for the study of T-PLL prompted us to perform similar experiments in the SUP-T11 cell line, validating its potential by showing an increased apoptotic rate. Taken together, these findings open new avenues for investigating the molecular mechanisms underlying the efficacy of PI in T-PLL and expand the spectrum of potential therapeutic strategies for this highly aggressive disease.

Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure

Exposure to various chemicals found in the environment and in the context of drug development can cause acute toxicity. To provide an alternative to in vivo animal toxicity testing, the U.S. Tox21 consortium developed in vitro assays to test a library of approximately 10,000 drugs and environmental chemicals (Tox21 10K compound library) in a quantitative high-throughput screening (qHTS) approach. In this study, we assessed the utility of Tox21 assay data in comparison with chemical structure information in predicting acute systemic toxicity. Prediction models were developed using four machine learning algorithms, namely Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Support Vector Machine, and their performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC). The chemical structure-based models as well as the Tox21 assay data demonstrated good predictive power for acute toxicity, achieving AUC-ROC values ranging from 0.83 to 0.93 and 0.73 to 0.79, respectively. We applied the models to predict the acute toxicity potential of the compounds in the Tox21 10K compound library, most of which were found to be non-toxic. In addition, we identified the Tox21 assays that contributed the most to acute toxicity prediction, such as acetylcholinesterase (AChE) inhibition and p53 induction. Chemical features including organophosphates and carbamates were also identified to be significantly associated with acute toxicity. In conclusion, this study underscores the utility of in vitro assay data in predicting acute toxicity.

Evaluating the Utility of the MSTI Assay in Predicting Compound Promiscuity and Cytotoxicity

Nonspecific reactive chemicals often interfere with the interpretation of high-throughput assay results because of their promiscuity and/or cytotoxicity. Using a high-throughput assay to identify such compounds is necessary to efficiently rule out potential assay artifacts. The MSTI, (E)-2-(4-mercaptostyryl)-1,3,3-trimethyl-3H-indol-1-ium, assay uses a thiol-containing fluorescent probe to screen for electrophile reactivity and could potentially be used to determine nonspecific reactive compounds. The Tox21 10K compound library was previously screened against a panel of ∼80 cell-based and biochemical assays, including the biochemical MSTI assay. In this study, we compared the MSTI assay activity of the Tox21 10K compounds with their promiscuity and cytotoxicity as reflected by their activities across the Tox21 assay panel to determine: (1) if this assay is predictive of a compound's promiscuity and cytotoxicity and (2) what chemical features create inconsistent results between the MSTI assay activity and promiscuity/cytotoxicity (false negatives and false positives). We found that the MSTI assay can predict a chemical's promiscuity/cytotoxicity with a 0.55 sensitivity and 0.97 specificity. Out of 3,407 unique compounds evaluated, we identified 92 false positive and 227 false negative results. Several structural features such as carboxamides and alkyl halides were found to be apparent in 53% (p = 2.4 × 10-07) and 19% (p = 4.3 × 10-06) of the false positives and negatives, respectively. The results of this analysis will help identify the potential challenges of this high-throughput assay and allow researchers to identify if a compound will be cytotoxic or promiscuous in an efficient manner.

High-throughput screening for small-molecule stabilizers of misfolded glucocerebrosidase in Gaucher disease and Parkinson's disease

Glucocerebrosidase (GCase) is implicated in both a rare, monogenic disorder (Gaucher disease, GD) and a common, multifactorial condition (Parkinson's disease, PD); hence, it is an urgent therapeutic target. To identify correctors of severe protein misfolding and trafficking obstruction manifested by the pathogenic L444P-variant of GCase, we developed a suite of quantitative, high-throughput, cell-based assays. First, we labeled GCase with a small proluminescent HiBiT peptide reporter tag, enabling quantitation of protein stabilization in cells while faithfully maintaining target biology. TALEN-based gene editing allowed for stable integration of a single HiBiT-GBA1 transgene into an intragenic safe-harbor locus in GBA1-knockout H4 (neuroglioma) cells. This GD cell model was amenable to lead discovery via titration-based quantitative high-throughput screening and lead optimization via structure-activity relationships. A primary screen of 10,779 compounds from the NCATS bioactive collections identified 140 stabilizers of HiBiT-GCase-L444P, including both pharmacological chaperones (ambroxol and noninhibitory chaperone NCGC326) and proteostasis regulators (panobinostat, trans-ISRIB, and pladienolide B). Two complementary high-content imaging-based assays were deployed to triage hits: The fluorescence-quenched substrate LysoFix-GBA captured functional lysosomal GCase activity, while an immunofluorescence assay featuring antibody hGCase-1/23 directly visualized GCase lysosomal translocation. NCGC326 was active in both secondary assays and completely reversed pathological glucosylsphingosine accumulation. Finally, we tested the concept of combination therapy by demonstrating synergistic actions of NCGC326 with proteostasis regulators in enhancing GCase-L444P levels. Looking forward, these physiologically relevant assays can facilitate the identification, pharmacological validation, and medicinal chemistry optimization of small molecules targeting GCase, ultimately leading to a viable therapeutic for GD and PD.

Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines

With the COVID-19 pandemic, the importance of vaccines has been widely recognized and has led to increased research and development efforts. Vaccines also play a crucial role in cancer treatment by activating the immune system to target and destroy cancer cells. However, enhancing the efficacy of cancer vaccines remains a challenge. Adjuvants, which enhance the immune response to antigens and improve vaccine effectiveness, have faced limitations in recent years, resulting in few novel adjuvants being identified. The advancement of artificial intelligence (AI) technology in drug development has provided a foundation for adjuvant screening and application, leading to a diversification of adjuvants. This article reviews the significant role of tumor vaccines in basic research and clinical treatment and explores the use of AI technology to screen novel adjuvants from databases. The findings of this review offer valuable insights for the development of new adjuvants for next-generation vaccines.

A Comprehensive Collection of Pain and Opioid Use Disorder Compounds for High-Throughput Screening and Artificial Intelligence-Driven Drug Discovery

As part of the NIH Helping to End Addiction Long-term (HEAL) Initiative, the National Center for Advancing Translational Sciences is dedicated to the development of new pharmacological tools and investigational drugs for managing and treating pain as well as the prevention and treatment of opioid misuse and addiction. In line with these objectives, we created a comprehensive, annotated small molecule library including drugs, probes, and tool compounds that act on published pain- and addiction-relevant targets. Nearly 3000 small molecules associated with approximately 200 known and hypothesized HEAL targets have been assembled, curated, and annotated in one collection. Physical samples of the library compounds have been acquired and plated in 1536-well format, enabling a rapid and efficient high-throughput screen against a wide range of assays. The creation of the HEAL Targets and Compounds Library, coupled with an integrated computational platform for AI-driven machine learning, structural modeling, and virtual screening, provides a valuable source for strategic drug repurposing, innovative profiling, and hypothesis testing of novel targets related to pain and opioid use disorder (OUD). The library is available to investigators for screening pain and OUD-relevant phenotypes.

Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor

Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here, we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILES strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured Emax values at the human μOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5 ± 3.9% and 91.8 ± 4.4%, respectively. To overcome the challenge of a small data set, a student-teacher learning method called tritraining with disagreement was tested using an unlabeled data set comprised of 15,816 ligands of human, mouse, and rat μOR, κOR, and δOR. We found that the tritraining scheme was able to increase the hold-out AUC of MPNN models to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of μOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.

Small-Molecule Disruptors of the Interaction between Calcium- and Integrin-Binding Protein 1 and Integrin αIIbβ3 as Novel Antiplatelet Agents

Thrombosis, a key factor in most cardiovascular diseases, is a major contributor to human mortality. Existing antithrombotic agents carry a risk of bleeding. Consequently, there is a keen interest in discovering innovative antithrombotic agents that can prevent thrombosis without negatively impacting hemostasis. Platelets play crucial roles in both hemostasis and thrombosis. We have previously characterized calcium- and integrin-binding protein 1 (CIB1) as a key regulatory molecule that regulates platelet function. CIB1 interacts with several platelet proteins including integrin αIIbβ3, the major glycoprotein receptor for fibrinogen on platelets. Given that CIB1 regulates platelet function through its interaction with αIIbβ3, we developed a fluorescence polarization (FP) assay to screen for potential inhibitors. The assay was miniaturized to 1536-well and screened in quantitative high-throughput screening (qHTS) format against a diverse compound library of 14,782 compounds. After validation and selectivity testing using the FP assay, we identified 19 candidate inhibitors and validated them using an in-gel binding assay that monitors the interaction of CIB1 with αIIb cytoplasmic tail peptide, followed by testing of top hits by intrinsic tryptophan fluorescence (ITF) and microscale thermophoresis (MST) to ascertain their interaction with CIB1. Two of the validated hits shared similar chemical structures, suggesting a common mechanism of action. Docking studies further revealed promising interactions within the hydrophobic binding pocket of the target protein, particularly forming key hydrogen bonds with Ser180. The compounds exhibited a potent antiplatelet activity based on their inhibition of thrombin-induced human platelet aggregation, thus indicating that disruptors of the CIB1- αIIbβ3 interaction could carry a translational potential as antithrombotic agents.

Comprehensive analysis of hub genes associated with cisplatin-resistance in ovarian cancer and screening of therapeutic drugs through bioinformatics and experimental validation

BACKGROUND

To identify key genes associated with cisplatin resistance in ovarian cancer, a comprehensive analysis was conducted on three datasets from the GEO database and through experimental validation.

METHODS

Gene expression profiles were retrieved from the GEO database. DEGs were identified by comparing gene expression profiles between cisplatin-sensitive and resistant ovarian cancer cell lines. The identified genes were further subjected to GO, KEGG, and PPI network analysis. Potential inhibitors of key genes were identified through methods such as LibDock nuclear molecular docking. In vitro assays and RT-qPCR were performed to assess the expression levels of key genes in ovarian cancer cell lines. The sensitivity of cells to chemotherapy and proliferation of key gene knockout cells were evaluated through CCK8 and Clonogenic assays.

RESULTS

Results showed that 12 genes influenced the chemosensitivity of the ovarian cancer cell line SKOV3, and 9 genes were associated with the prognosis and survival outcomes of ovarian cancer patients. RT-qPCR results revealed NDRG1, CYBRD1, MT2A, CNIH3, DPYSL3, and CARMIL1 were upregulated, whereas ERBB4, ANK3, B2M, LRRTM4, EYA4, and SLIT2 were downregulated in cisplatin-resistant cell lines. NDRG1, CYBRD1, and DPYSL3 knock-down significantly inhibited the proliferation of cisplatin-resistant cell line SKOV3. Finally, photofrin, a small-molecule compound targeting CYBRD1, was identified.

CONCLUSION

This study reveals changes in the expression level of some genes associated with cisplatin-resistant ovarian cancer. In addition, a new small molecule compound was identified for the treatment of cisplatin-resistant ovarian cancer.

Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor

Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILE strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured E max values at the human μOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5±3.9% and 91.8± 4.4%, respectively. To overcome the challenge of small dataset, a student-teacher learning method called tri-training with disagreement was tested using an unlabeled dataset comprised of 15,816 ligands of human, mouse, or rat μOR, κOR, or δOR. We found that the tri-training scheme was able to increase the hold-out AUC of MPNN to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of μOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.

Integrative analysis discovers Imidurea as dual multitargeted inhibitor of CD69, CD40, SHP2, lysozyme, GATA3, cCBL, and S-cysteinase from SARS-CoV-2 and M. tuberculosis

Two of the deadliest infectious diseases, COVID-19 and tuberculosis (TB), have combined to establish a worldwide pandemic, wreaking havoc on economies and claiming countless lives. The optimised, multitargeted medications may diminish resistance and counter them together. Based on computational expression studies, 183 genes were co-expressed in COVID-19 and TB blood samples. We used the multisampling screening algorithms on the top ten co-expressed genes (CD40, SHP2, Lysozyme, GATA3, cCBL, SIVmac239 Nef, CD69, S-adenosylhomocysteinase, Chemokine Receptor-7, and Membrane Protein). Imidurea is a multitargeted inhibitor for COVID-19 and TB, as confirmed by extensive screening and post-filtering utilising MM\GBSA algorithms. Imidurea has shown docking and MM\GBSA scores of -8.21 to -4.75 Kcal/mol and -64.16 to -29.38 Kcal/mol, respectively. The DFT, pharmacokinetics, and interaction patterns suggest that Imidurea may be a drug candidate, and all ten complexes were tested for stability and bond strength using 100 ns for all MD atoms. The modelling findings showed the complex's repurposing potential, with a cumulative deviation and fluctuation of <2 Å and significant intermolecular interaction, which validated the possibilities. Finally, an inhibition test was performed to confirm our in-silico findings on SARS-CoV-2 Delta variant infection, which was suppressed by adding imidurea to Vero E6 cells after infection.

Chlordiazepoxide against signalling, receptor and regulatory proteins of breast cancer: a structure-based in-silico approach

Among the most prevalent forms of cancer are breast, lung, colon-rectum, and prostate cancers, and breast cancer is a major global health challenge, contributing to 2.26 million cases with approximately 685,000 deaths worldwide in 2020 alone, typically beginning in the milk ducts or lobules that produce and transport milk during lactation and it is becoming challenging to treat as the tissues are developing resistance, which makes urgent calls for new multitargeted drugs. The multitargeted drug design provides a better solution, simultaneously targeting multiple pathways, even when the drug resists one, it remains effective for others. In this study, we included four crucial proteins that perform signalling, receptor, and regulatory action, namely- NUDIX Hydrolases, Dihydrofolate Reductase, HER2/neu Kinase and EGFR and performed multitargeted molecular docking studies against human-approved drugs using HTVS, SP and extra precise algorithms and filtered the poses with MM\GBSA, suggested a benzodiazepine derivative chlordiazepoxide, used as an anxiolytic agent, can be a multitargeted inhibitor with docking and MM\GBSA score ranging from - 4.628 to - 7.877 and - 18.59 to - 135.86 kcal/mol, respectively, and the most interacted residues were 6ARG, 6GLU, 3TRP, and 3VAL. The QikProp-based ADMET and DFT computations showed the suitability and stability of the drug candidate followed by 100 ns MD simulation in water and MMGBSA on trajectories, resulting in stable performance and many intermolecular interactions to make the complexes stable, which favours that chlordiazepoxide can be a multitargeted breast cancer inhibitor. However, experimental validation is needed before its use.

Development of quantitative high-throughput screening assays to identify, validate, and optimize small-molecule stabilizers of misfolded β-glucocerebrosidase with therapeutic potential for Gaucher disease and Parkinson's disease

Glucocerebrosidase (GCase) is implicated in both a rare, monogenic disorder (Gaucher disease, GD) and a common, multifactorial condition (Parkinson's disease); hence, it is an urgent therapeutic target. To identify correctors of severe protein misfolding and trafficking obstruction manifested by the pathogenic L444P-variant of GCase, we developed a suite of quantitative, high-throughput, cell-based assays. First, we labeled GCase with a small pro-luminescent HiBiT peptide reporter tag, enabling quantitation of protein stabilization in cells while faithfully maintaining target biology. TALEN-based gene editing allowed for stable integration of a single HiBiT-GBA1 transgene into an intragenic safe-harbor locus in GBA1-knockout H4 (neuroglioma) cells. This GD cell model was amenable to lead discovery via titration-based quantitative high-throughput screening and lead optimization via structure-activity relationships. A primary screen of 10,779 compounds from the NCATS bioactive collections identified 140 stabilizers of HiBiT-GCase-L444P, including both pharmacological chaperones (ambroxol and non-inhibitory chaperone NCGC326) and proteostasis regulators (panobinostat, trans-ISRIB, and pladienolide B). Two complementary high-content imaging-based assays were deployed to triage hits: the fluorescence-quenched substrate LysoFix-GBA captured functional lysosomal GCase activity, while an immunofluorescence assay featuring antibody hGCase-1/23 provided direct visualization of GCase lysosomal translocation. NCGC326 was active in both secondary assays and completely reversed pathological glucosylsphingosine accumulation. Finally, we tested the concept of combination therapy, by demonstrating synergistic actions of NCGC326 with proteostasis regulators in enhancing GCase-L444P levels. Looking forward, these physiologically-relevant assays can facilitate the identification, pharmacological validation, and medicinal chemistry optimization of new chemical matter targeting GCase, ultimately leading to a viable therapeutic for two protein-misfolding diseases.

Structure-Based In Silico Approaches Reveal IRESSA as a Multitargeted Breast Cancer Regulatory, Signalling, and Receptor Protein Inhibitor

Breast cancer begins in the breast cells, mainly impacting women. It starts in the cells that line the milk ducts or lobules responsible for producing milk and can spread to nearby tissues and other body parts. In 2020, around 2.3 million women across the globe received a diagnosis, with an estimated 685,000 deaths. Additionally, 7.8 million women were living with breast cancer, making it the fifth leading cause of cancer-related deaths among women. The mutational changes, overexpression of drug efflux pumps, activation of alternative signalling pathways, tumour microenvironment, and cancer stem cells are causing higher levels of drug resistance, and one of the major solutions is to identify multitargeted drugs. In our research, we conducted a comprehensive screening using HTVS, SP, and XP, followed by an MM/GBSA computation of human-approved drugs targeting HER2/neu, BRCA1, PIK3CA, and ESR1. Our analysis pinpointed IRESSA (Gefitinib-DB00317) as a multitargeted inhibitor for these proteins, revealing docking scores ranging from -4.527 to -8.809 Kcal/mol and MM/GBSA scores between -49.09 and -61.74 Kcal/mol. We selected interacting residues as fingerprints, pinpointing 8LEU, 6VAL, 6LYS, 6ASN, 5ILE, and 5GLU as the most prevalent in interactions. Subsequently, we analysed the ADMET properties and compared them with the standard values of QikProp. We extended our study for DFT computations with Jaguar and plotted the electrostatic potential, HOMO and LUMO regions, and electron density, followed by a molecular dynamics simulation for 100 ns in water, showing an utterly stable performance, making it a suitable drug candidate. IRESSA is FDA-approved for lung cancer, which shares some pathways with breast cancers, clearing the hurdles of multitargeted drugs against breast and lung cancer. This has the potential to be groundbreaking; however, more studies are needed to concreate IRESSA's role.

An Expedition on Synthetic Methodology of FDA-approved Anticancer Drugs (2018-2021)

New drugs being established in the market every year produce specified structures for selective biological targeting. With medicinal insights into molecular recognition, these begot molecules open new rooms for designing potential new drug molecules. In this review, we report the compilation and analysis of a total of 56 drugs including 33 organic small molecules (Mobocertinib, Infigratinib, Sotorasib, Trilaciclib, Umbralisib, Tepotinib, Relugolix, Pralsetinib, Decitabine, Ripretinib, Selpercatinib, Capmatinib, Pemigatinib, Tucatinib, Selumetinib, Tazemetostat, Avapritinib, Zanubrutinib, Entrectinib, Pexidartinib, Darolutamide, Selinexor, Alpelisib, Erdafitinib, Gilteritinib, Larotrectinib, Glasdegib, Lorlatinib, Talazoparib, Dacomitinib, Duvelisib, Ivosidenib, Apalutamide), 6 metal complexes (Edotreotide Gallium Ga-68, fluoroestradiol F-18, Cu 64 dotatate, Gallium 68 PSMA-11, Piflufolastat F-18, 177Lu (lutetium)), 16 macromolecules as monoclonal antibody conjugates (Brentuximabvedotin, Amivantamab-vmjw, Loncastuximabtesirine, Dostarlimab, Margetuximab, Naxitamab, Belantamabmafodotin, Tafasitamab, Inebilizumab, SacituzumabGovitecan, Isatuximab, Trastuzumab, Enfortumabvedotin, Polatuzumab, Cemiplimab, Mogamulizumab) and 1 peptide enzyme (Erwiniachrysanthemi-derived asparaginase) approved by the U.S. FDA between 2018 to 2021. These drugs act as anticancer agents against various cancer types, especially non-small cell lung, lymphoma, breast, prostate, multiple myeloma, neuroendocrine tumor, cervical, bladder, cholangiocarcinoma, myeloid leukemia, gastrointestinal, neuroblastoma, thyroid, epithelioid and cutaneous squamous cell carcinoma. The review comprises the key structural features, approval times, target selectivity, mechanisms of action, therapeutic indication, formulations, and possible synthetic approaches of these approved drugs. These crucial details will benefit the scientific community for futuristic new developments in this arena.

Discovery of druggable potent inhibitors of serine proteases and farnesoid X receptor by ligand-based virtual screening to obstruct SARS-CoV-2

The coronavirus, a subfamily of the coronavirinae family, is an RNA virus with over 40 variations that can infect humans, non-human mammals and birds. There are seven types of human coronaviruses, including SARS-CoV-2, is responsible for the recent COVID-19 pandemic. The current study is focused on the identification of drug molecules for the treatment of COVID-19 by targeting human proteases like transmembrane serine protease 2 (TMPRSS2), furin, cathepsin B, and a nuclear receptor named farnesoid X receptor (FXR). TMPRSS2 and furin help in cleaving the spike protein of the SARS-CoV-2 virus, while cathepsin B plays a critical role in the entry and pathogenesis. FXR, on the other hand, regulates the expression of ACE2, and its inhibition can reduce SARS-CoV-2 infection. By inhibiting these four protein targets with non-toxic inhibitors, the entry of the infectious agent into host cells and its pathogenesis can be obstructed. We have used the BioSolveIT suite for pharmacophore-based computational drug designing. A total of 1611 ligands from the ligand library were docked with the target proteins to obtain potent inhibitors on the basis of pharmacophore. Following the ADMET analysis and protein ligand interactions, potent and druggable inhibitors of the target proteins were obtained. Additionally, toxic substructures and the less toxic route of administration of the most potent inhibitors in rodents were also determined computationally. Compounds namely N-(diaminomethylene)-2-((3-((1R,3R)-3-(2-(methoxy(methyl)amino)-2-oxoethyl)cyclopentyl)propyl)amino)-2-oxoethan-1-aminium (26), (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((4-propyl-1H-imidazol-2-yl)methyl)piperidin-1-ium (29) and (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((1-propyl-1H-pyrazol-4-yl)methyl)piperidin-1-ium (30) were found as the potent inhibitors of TMPRSS2, whereas, 1-(1-(1-(1H-tetrazol-1-yl)cyclopropane-1‑carbonyl)piperidin-4-yl)azepan-2-one (6), (2R)-4-methyl-1-oxo-1-((7R,11S)-4-oxo-6,7,8,9,10,11-hexahydro-4H-7,11-methanopyrido[1,2-a]azocin-9-yl)pentan-2-aminium (12), 4-((1-(3-(3,5-dimethylisoxazol-4-yl)propanoyl)piperidin-4-yl)methyl)morpholin-4-ium (13), 1-(4,6-dimethylpyrimidin-2-yl)-N-(3-oxocyclohex-1-en-1-yl)piperidine-4-carboxamide (14), 1-(4-(1,5-dimethyl-1H-1,2,4-triazol-3-yl)piperidin-1-yl)-3-(3,5-dimethylisoxazol-4-yl)propan-1-one (25) and N,N-dimethyl-4-oxo-4-((1S,5R)-8-oxo-5,6-dihydro-1H-1,5-methanopyrido[1,2-a][1,5]diazocin-3(2H,4H,8H)-yl)butanamide (31) inhibited the FXR preferentially. In case of cathepsin B, N-((5-benzoylthiophen-2-yl)methyl)-2-hydrazineyl-2-oxoacetamide (2) and N-([2,2'-bifuran]-5-ylmethyl)-2-hydrazineyl-2-oxoacetamide (7) were identified as the most druggable inhibitors whereas 1-amino-2,7-diethyl-3,8-dioxo-6-(p-tolyl)-2,3,7,8-tetrahydro-2,7-naphthyridine-4‑carbonitrile (5) and (R)-6-amino-2-(2,3-dihydroxypropyl)-1H-benzo[de]isoquinoline-1,3(2H)-dione (20) were active against furin.

Molecular Graph-Based Deep Learning Algorithm Facilitates an Imaging-Based Strategy for Rapid Discovery of Small Molecules Modulating Biomolecular Condensates

Biomolecular condensates are proposed to cause diseases, such as cancer and neurodegeneration, by concentrating proteins at abnormal subcellular loci. Imaging-based compound screens have been used to identify small molecules that reverse or promote biomolecular condensates. However, limitations of conventional imaging-based methods restrict the screening scale. Here, we used a graph convolutional network (GCN)-based computational approach and identified small molecule candidates that reduce the nuclear liquid-liquid phase separation of TAR DNA-binding protein 43 (TDP-43), an essential protein that undergoes phase transition in neurodegenerative diseases. We demonstrated that the GCN-based deep learning algorithm is suitable for spatial information extraction from the molecular graph. Thus, this is a promising method to identify small molecule candidates with novel scaffolds. Furthermore, we validated that these candidates do not affect the normal splicing function of TDP-43. Taken together, a combination of an imaging-based screen and a GCN-based deep learning method dramatically improves the speed and accuracy of the compound screen for biomolecular condensates.

Masoprocol: a promising candidate for targeting insulin resistance by inhibiting resistin with optimal druglikeness Potentials: an in silico approach

Resistin is a cysteine-rich secretory hormone that induces resistance to insulin, and its elevated expression is correlated with the onset of diabetes and several related metabolic disorders. Resistin performs its inhibitory role by connecting three identical subunits through Cys22-based disulfide linkages. The necessity to inhibit the formation of resistin trimer is one of the essential means to prevent the aggravation of diabetes mellitus type 2, obesity, and atherosclerosis. This study was conducted to screen the clinically approved drugs to find the most potent one to inhibit resistin with the best pharmacokinetics and drug-likeness properties. A total of 4654 clinically approved drugs were docked against the Cys22 residue of resistin. The top ten drugs with the highest high-precision (XP) docking scores were selected. Ioversol and masoprocol showed the highest XP docking and Molecular Mechanics-Generalized Born Surface Area (MMGBSA) scores, respectively, with double hydrogen bonding with the targeted Cys22. Molecular dynamics (MD) simulations showed that the masoprocol-resistin complex exhibited lower root mean square deviation (RMSD), radius of gyration, and root mean square fluctuation (RMSF) values than those observed in the ioversol-resistin complex. Both drugs induced drastic conformational changes in resistin monomer interactions. However, ioversol did not prove satisfying drug-likeness properties, while masoprocol showed the most favourable pharmacokinetic and drug-likeness properties. This study has demonstrated that masoprocol offers a novel inhibitory effect on resistin with the highest ligand affinity, making it a promising drug for combating insulin resistance.Communicated by Ramaswamy H. Sarma.

Identification of Potent Inhibitors Targeting EGFR and HER3 for Effective Treatment of Chemoresistance in Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is the most common form of lung cancer. Despite the existence of various therapeutic options, NSCLC is still a major health concern due to its aggressive nature and high mutation rate. Consequently, HER3 has been selected as a target protein along with EGFR because of its limited tyrosine kinase activity and ability to activate PI3/AKT pathway responsible for therapy failure. We herein used a BioSolveIT suite to identify potent inhibitors of EGFR and HER3. The schematic process involves screening of databases for constructing compound library comprising of 903 synthetic compounds (602 for EGFR and 301 for HER3) followed by pharmacophore modeling. The best docked poses of compounds with the druggable binding site of respective proteins were selected according to pharmacophore designed by SeeSAR version 12.1.0. Subsequently, preclinical analysis was performed via an online server SwissADME and potent inhibitors were selected. Compound 4k and 4m were the most potent inhibitors of EGFR while 7x effectively inhibited the binding site of HER3. The binding energies of 4k, 4m, and 7x were -7.7, -6.3 and -5.7 kcal/mol, respectively. Collectively, 4k, 4m and 7x showed favorable interactions with the most druggable binding sites of their respective proteins. Finally, in silico pre-clinical testing by SwissADME validated the non-toxic nature of compounds 4k, 4m and 7x providing a promising treatment option for chemoresistant NSCLC.

How Many Chemicals in Commerce Have Been Analyzed in Environmental Media? A 50 Year Bibliometric Analysis

Over the past 50 years, there has been a tremendous expansion in the measurement of chemical contaminants in environmental media. But how many chemicals have actually been determined, and do they represent a significant fraction of substances in commerce or of chemicals of concern? To address these questions, we conducted a bibliometric survey to identify what individual chemicals have been determined in environmental media and their trends over the past 50 years. The CAplus database of CAS, a Division of the American Chemical Society, was searched for indexing roles "analytical study" and "pollutant" yielding a final list of 19,776 CAS Registry Numbers (CASRNs). That list was then used to link the CASRNs to biological studies, yielding a data set of 9.251 × 10⁶ total counts of the CASRNs over a 55 year period. About 14,150 CASRNs were substances on various priority lists or their close analogs and transformation products. The top 100 most reported CASRNs accounted for 34% of the data set, confirming previous studies showing a significant bias toward repeated measurements of the same substances due to regulatory needs and the challenges of determining new, previously unmeasured, compounds. Substances listed in the industrial chemical inventories of Europe, China, and the United States accounted for only about 5% of measured substances. However, pharmaceuticals and current use pesticides were widely measured accounting for 50-60% of total CASRN counts for the period 2000-2015.

Selective pharmacological inhibition alters human carcinoma lung cell-derived extracellular vesicle formation

Exosomes also termed small extracellular vesicles (sEVs) are important mediators of intercellular communication in many physiological and pathological processes such as protein clearance, immunity, infections, signaling, and cancer. Elevated circulating levels of exosomes have been linked to some viral infections, aggressive cancer, and neurodegenerative diseases. Some pharmacological compounds have been demonstrated to effectively inhibit exosome production pathways. There are very few studies on exosome inhibition and how they influence pathophysiological conditions.

Methods

In the current study, we examined how inhibition of extracellular vesicle release and/or uptake would impact the exosome formation pathway. Using a constellation of improved EV experimental approaches, we evaluated the concentration-based cytotoxicity effects of pharmacological agents (ketoconazole, climbazole, and heparin) on Human Lung Carcinoma (A549) cell viability. We investigated the effect of inhibitor dosages on exosome production and release. Analysis of exosome inhibition includes quantitative analysis and total protein expression of exosome release after pharmacological inhibition; we examined exosome protein level after inhibition.

Results

Selective inhibition of exosomes altered particle sizes, and heparin significantly reduced the total exosomes released. Climbazole and heparin undermined membrane-bound tetraspanin CD63 expression and significantly disrupted ALIX protein (p ≤ 0.0001) and TSG101 (p ≤ 0.001). Azoles and heparin also disrupt transmembrane trafficking by modulating Ras binding protein (p ≤ 0.001).

Conclusion

These findings revealed that pharmacological inhibition of exosomes regulates the endocytic pathway and expression of endosomal sorting complex required for transport mediators, suggesting climbazole and heparin as effective inhibitors of exosome synthesis.

A novel chemogenomic discovery platform identifies bioactive hits with rapid bactericidal activity against Mycobacteroides Abscessus

Mycobacteroides abscessus (M. ab) infections are innately resistant to most currently available antibiotics and present a growing, poorly addressed medical need. The existing treatment regimens are lengthy and produce inadequate outcomes for many patients. Importantly, most clinically used drugs and drug candidates against M. ab are either bacteriostatic, or only weakly bactericidal. New strategies exploring a broader chemical space are urgently needed, as innovative agents in development are scarce and hit rates in large unbiased screens against the mycobacterium have been discouragingly low. Here we present a computational chemogenomics-driven approach to discovery of novel antibacterials that effectively reveals drug-like compounds active against M. ab, paired with small sets of predicted molecular targets for the compounds. Several of the bioactive hits identified exhibited rapid bactericidal, including sterilizing, activity against the mycobacterium, indicating that there are currently unexploited chemically tractable molecular mechanisms for rapid sterilization of M. ab. Interestingly, starvation, which typically induces drug tolerance, sensitized M. ab to some of the compounds, resulting in potencies similar to those of drugs in clinical use. The presented drug discovery platform has potential to identify highly differentiated prototype anti-infective molecules and thereby contribute to development of regimens for shorter treatment and improved outcomes for non-tuberculous mycobacterial infections.

In vivo quantitative high-throughput screening for drug discovery and comparative toxicology

Quantitative high-throughput screening (qHTS) pharmacologically evaluates chemical libraries for therapeutic uses, toxicological risk and, increasingly, for academic probe discovery. Phenotypic high-throughput screening assays interrogate molecular pathways, often relying on cell culture systems, historically less focused on multicellular organisms. Caenorhabditis elegans has served as a eukaryotic model organism for human biology by virtue of genetic conservation and experimental tractability. Here, a paradigm enabling C. elegans qHTS using 384-well microtiter plate laser-scanning cytometry is described, in which GFP-expressing organisms revealing phenotype-modifying structure-activity relationships guide subsequent life-stage and proteomic analyses, and Escherichia coli bacterial ghosts, a non-replicating nutrient source, allow compound exposures over two life cycles, mitigating bacterial overgrowth complications. We demonstrate the method with libraries of anti-infective agents, or substances of toxicological concern. Each was tested in seven-point titration to assess the feasibility of nematode-based in vivo qHTS, and examples of follow-up strategies were provided to study organism-based chemotype selectivity and subsequent network perturbations with a physiological impact. We anticipate that this qHTS approach will enable analysis of C. elegans orthologous phenotypes of human pathologies to facilitate drug library profiling for a range of therapeutic indications.

Predictive Models for Human Cytochrome P450 3A7 Selective Inhibitors and Substrates

Inappropriate use of prescription drugs is potentially more harmful in fetuses/neonates than in adults. Cytochrome P450 (CYP) 3A subfamily undergoes developmental changes in expression, such as a transition from CYP3A7 to CYP3A4 shortly after birth, which provides a potential way to distinguish medication effects on fetuses/neonates and adults. The purpose of this study was to build first-in-class predictive models for both inhibitors and substrates of CYP3A7/CYP3A4 using chemical structure analysis. Three metrics were used to evaluate model performance: area under the receiver operating characteristic curve (AUC-ROC), balanced accuracy (BA), and Matthews correlation coefficient (MCC). The performance varied for each CYP3A7/CYP3A4 inhibitor/substrate model depending on the data set type, model type, rebalancing method, and specific feature set. For the active inhibitor/substrate data set, the optimal models achieved AUC-ROC values ranging from 0.77 ± 0.01 to 0.84 ± 0.01. For the selective inhibitor/substrate data set, the optimal models achieved AUC-ROC values ranging from 0.72 ± 0.02 to 0.79 ± 0.04. The predictive power of the optimal models was validated by compounds with known potencies as CYP3A7/CYP3A4 inhibitors or substrates. In addition, we identified structural features significant for CYP3A7/CYP3A4 selective or common inhibitors and substrates. In summary, the top performing models can be further applied as a tool to rapidly evaluate the safety and efficacy of new drugs separately for fetuses/neonates and adults. The significant structural features could guide the design of new therapeutic drugs as well as aid in the optimization of existing medicine for fetuses/neonates.

Vanoxerine kills mycobacteria through membrane depolarization and efflux inhibition

Mycobacterium tuberculosis is a deadly pathogen, currently the leading cause of death worldwide from a single infectious agent through tuberculosis infections. If the End TB 2030 strategy is to be achieved, additional drugs need to be identified and made available to supplement the current treatment regimen. In addition, drug resistance is a growing issue, leading to significantly lower treatment success rates, necessitating further drug development. Vanoxerine (GBR12909), a dopamine re-uptake inhibitor, was recently identified as having anti-mycobacterial activity during a drug repurposing screening effort. However, its effects on mycobacteria were not well characterized. Herein, we report vanoxerine as a disruptor of the membrane electric potential, inhibiting mycobacterial efflux and growth. Vanoxerine had an undetectable level of resistance, highlighting the lack of a protein target. This study suggests a mechanism of action for vanoxerine, which will allow for its continued development or use as a tool compound.

Role of angiotensin receptor blockers in the context of Alzheimer's disease

As the world's population ages, the prevalence of age-related neurological disorders such as Alzheimer's disease (AD) is increasing. There is currently no treatment for Alzheimer's disease, and the few approved medications have a low success rate in lowering symptoms. As a result, several attempts are underway worldwide to identify new targets for the therapy of Alzheimer's disease. In preclinical studies of Alzheimer's disease, it was recently found that inhibition of angiotensin-converting enzyme (ACE) and blocking of the angiotensin II receptors reduce symptoms of neurodegeneration, Aβ plaque development, and tau hyperphosphorylation. Angiotensin II type I (AT1) blockers, such as telmisartan, candesartan, valsartan, and others, have a wide safety margin and are commonly used to treat hypertension. Renal and cardiovascular failures are reduced due to their vascular protective actions. Inhibition of AT1 receptors in the brain has a neuroprotective impact in humans, reducing the risk of stroke, increasing cognition, and slowing the progression of Alzheimer's disease. The review focuses on the mechanisms via which AT1 blockers may act beneficially in Alzheimer's disease. Although their effect is evident in preclinical studies, clinical trials, on the other hand, are in short supply to validate the strategy. More dose-response experiments with possible AT1 blockers and brain-targeted administration will be needed in the future.

Imipramine and olanzapine block apoE4-catalyzed polymerization of Aβ and show evidence of improving Alzheimer’s disease cognition

Background

The apolipoprotein E (APOE) ε4 allele confers the strongest risk for late-onset Alzheimer’s disease (AD) besides age itself, but the mechanisms underlying this risk are debated. One hypothesis supported by evidence from multiple labs is that apoE4 binds to the amyloid-β (Aβ) peptide and catalyzes its polymerization into neurotoxic oligomers and fibrils. Inhibiting this early step in the amyloid cascade may thereby reduce or prevent neurodegeneration and AD.

Methods

Using a design of experiments (DOE) approach, we developed a high-throughput assay to identify inhibitors of apoE4-catalyzed polymerization of Aβ into oligomers and fibrils. We used it to screen the NIH Clinical Collection of small molecule drugs tested previously in human clinical trials. We then evaluated the efficacy and cytotoxicity of the hit compounds in primary neuron models of apoE4-induced Aβ and phosphorylated tau aggregation. Finally, we performed retrospective analyses of the National Alzheimer’s Coordinating Center (NACC) clinical dataset, using Cox regression and Cox proportional hazards models to determine if the use of two FDA-approved hit compounds was associated with better cognitive scores (Mini-Mental State Exam), or improved AD clinical diagnosis, when compared with other medications of the same clinical indication.

Results

Our high-throughput screen identified eight blood-brain barrier (BBB)-permeable hit compounds that reduced apoE4-catalyzed Aβ oligomer and fibril formation in a dose-dependent manner. Five hit compounds were non-toxic toward cultured neurons and also reduced apoE4-promoted Aβ and tau neuropathology in a dose-dependent manner. Three of the five compounds were determined to be specific inhibitors of apoE4, whereas the other two compounds were Aβ or tau aggregation inhibitors. When prescribed to AD patients for their normal clinical indications, two of the apoE4 inhibitors, imipramine and olanzapine, but not other (non-hit) antipsychotic or antidepressant medications, were associated with improvements in cognition and clinical diagnosis, especially among APOE4 carriers.

Conclusions

The critical test of any proposed AD mechanism is whether it leads to effective treatments. Our high-throughput screen identified two promising FDA-approved drugs, imipramine and olanzapine, which have no structural, functional, or clinical similarities other than their shared ability to inhibit apoE4-catalyzed Aβ polymerization, thus identifying this mechanism as an essential contribution of apoE4 to AD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-01020-9.

Terfenadine resensitizes doxorubicin activity in drug-resistant ovarian cancer cells via an inhibition of CaMKII/CREB1 mediated ABCB1 expression

Ovarian cancer is one of the most lethal gynecological malignancies. Recurrence or acquired chemoresistance is the leading cause of ovarian cancer therapy failure. Overexpression of ATP-binding cassette subfamily B member 1 (ABCB1), commonly known as P-glycoprotein, correlates closely with multidrug resistance (MDR). However, the mechanism underlying aberrant ABCB1 expression remains unknown. Using a quantitative high-throughput combinational screen, we identified that terfenadine restored doxorubicin sensitivity in an MDR ovarian cancer cell line. In addition, RNA-seq data revealed that the Ca2+-mediated signaling pathway in the MDR cells was abnormally regulated. Moreover, our research demonstrated that terfenadine directly bound to CAMKIID to prevent its autophosphorylation and inhibit the activation of the cAMP-responsive element-binding protein 1 (CREB1)-mediated pathway. Direct inhibition of CAMKII or CREB1 had the same phenotypic effects as terfenadine in the combined treatment, including lower expression of ABCB1 and baculoviral IAP repeat-containing 5 (BIRC5, also known as survivin) and increased doxorubicin-induced apoptosis. In this study, we demonstrate that aberrant regulation of the Ca2+-mediated CAMKIID/CREB1 pathway contributes to ABCB1 over-expression and MDR creation and that CAMKIID and CREB1 are attractive targets for restoring doxorubicin efficacy in ABCB1-mediated MDR ovarian cancer.

Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data

Drug-induced liver injury (DILI) and cardiotoxicity (DICT) are major adverse effects triggered by many clinically important drugs. To provide an alternative to in vivo toxicity testing, the U.S. Tox21 consortium has screened a collection of ∼10K compounds, including drugs in clinical use, against >70 cell-based assays in a quantitative high-throughput screening (qHTS) format. In this study, we compiled reference compound lists for DILI and DICT and compared the potential of Tox21 assay data with chemical structure information in building prediction models for human in vivo hepatotoxicity and cardiotoxicity. Models were built with four different machine learning algorithms (e.g., Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Support Vector Machine) and model performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC-ROC). Chemical structure-based models showed reasonable predictive power for DILI (best AUC-ROC = 0.75 ± 0.03) and DICT (best AUC-ROC = 0.83 ± 0.03), while Tox21 assay data alone only showed better than random performance. DILI and DICT prediction models built using a combination of assay data and chemical structure information did not have a positive impact on model performance. The suboptimal predictive performance of the assay data is likely due to insufficient coverage of an adequately predictive number of toxicity mechanisms. The Tox21 consortium is currently expanding coverage of biological response space with additional assays that probe toxicologically important targets and under-represented pathways that may improve the prediction of in vivo toxicity such as DILI and DICT.

Pixantrone confers radiosensitization in KRAS mutated cancer cells by suppression of radiation-induced prosurvival pathways

Radioresistance towards radiation therapy has generated the need for the development of radiosensitizers as a potential drug. KRAS mutation brings radioresistance in tumor cells. The present work proves sensitization of cancer cells towards radiotherapy through inhibition of KRAS activation. Acquiring a drug repurposing approach, the in-silico screening revealed that pixantrone, an antineoplastic drug, possesses a high affinity towards KRAS G12C and G12D subtypes. The SPR study suggests that maximum affinity of pixantrone was observed with KRAS G12C>WT>G12D and G12S. Pixantrone potentially inhibited the KRAS activation in stable transfectants G12C and G12D cell lines and radiosensitized distinct KRAS mutant subtype cells. The combination of pixantrone with radiation causes enhanced dsDNA breaks along with enhanced ATM expression, and increased late apoptosis. The preclinical studies on NCr-fox1^nu xenograft mice showed potent inhibition of tumor progression and prolonged survival of mcie due to the radiosensitizing effect of pixantrone. Radiation-induced activation of key effector proteins of RAS downstream pathways, like MAPK and PI3K/Akt/mTOR pathways, were downregulated in tumor cells upon combination treatment. Interestingly, a robust upregulation of senescence marker p21 was observed in the tumor cells in combination treatment. These findings reveal a convergence between KRAS signaling, pixantrone treatment, and radiation conferring tumor cell death.

Small-molecule high-throughput screening identifies a MEK inhibitor PD1938306 that enhances sorafenib efficacy via MCL-1 and BIM in hepatocellular carcinoma cells

Background Sorafenib is the most widely used systematic therapy drug for treating unresectable hepatocellular carcinoma (HCC) but showed dissatisfactory efficacy in clinical applications. Objective We conducted a combinational quantitative small-molecule high-throughput screening (qHTS) to identify potential candidates to enhance the treatment effectiveness of sorafenib. Methods First, using a Hep3B human HCC cell line, 7051 approved drugs and bioactive compounds were screened, then the primary hits were tested with/ without 0.5 μM sorafenib respectively, the compound has the half maximal inhibitory concentration (IC50) shift value greater than 1.5 was thought to have the synergistic effect with sorafenib. Furthermore, the MEK inhibitor PD198306 was selected for further mechanistic study. Results 12 effective compounds were identified, including kinase inhibitors that target MEK, AURKB, CAMK, ROCK2, BRAF, PI3K, AKT and EGFR, as well as a μ-opioid receptor agonist and a L-type calcium channel blocker. The mechanistic research of the combination of sorafenib plus PD198306 showed that the two compounds synergistically inhibited MEK-ERK and mTORC1-4EBP1, and induced apoptosis in HCC cells, which can be attributed to the transcriptional and posttranslational regulation of MCL-1 and BIM. Conclusion Small-molecule qHTS identifies MEK inhibitor PD1938306 as a potent sorafenib enhancer, together with several novel combination strategies that are valuable for further studies.

Pharmacophore-Model-Based Drug Repurposing for the Identification of the Potential Inhibitors Targeting the Allosteric Site in Dengue Virus NS5 RNA-Dependent RNA Polymerase

Dengue virus (DENV) is the causative agent of DENV infection. To tackle DENV infection, the development of therapeutic molecules as direct-acting antivirals (DAAs) has been demonstrated as a truly effective approach. Among various DENV drug targets, non-structural protein 5 (NS5)-a highly conserved protein among the family Flaviviridae-carries the RNA-dependent RNA polymerase (DENV^RdRp) domain at the C-terminal, and its "N-pocket" allosteric site is widely considered for anti-DENV drug development. Therefore, in this study, we developed a pharmacophore model by utilising 41 known inhibitors of the DENV^RdRp domain, and performed model screening against the FDA's approved drug database for drug repurposing against DENV^RdRp. Herein, drugs complying with the pharmacophore hypothesis were further processed through standard-precision (SP) and extra-precision (XP) docking scores (DSs) and binding pose refinement based on MM/GBSA binding energy (BE) calculations. This resulted in the identification of four potential potent drugs: (i) desmopressin (DS: -10.52, BE: -69.77 kcal/mol), (ii) rutin (DS: -13.43, BE: -67.06 kcal/mol), (iii) lypressin (DS: -9.84, BE: -67.65 kcal/mol), and (iv) lanreotide (DS: -8.72, BE: -64.7 kcal/mol). The selected drugs exhibited relevant interactions with the allosteric N-pocket of DENV^RdRp, including priming-loop and entry-point residues (i.e., R729, R737, K800, and E802). Furthermore, 100 ns explicit-solvent molecular dynamics simulations and end-point binding free energy assessments support the considerable stability and free energy of the selected drugs in the targeted allosteric pocket of DENV^RdRp. Hence, these four drugs, repurposed as potent inhibitors of the allosteric site of DENV^RdRp, are recommended for further validation using experimental assays.

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

A quantitative high-throughput screen identifies compounds that lower expression of the SCA2-and ALS-associated gene ATXN2

CAG repeat expansions in the ATXN2 (ataxin-2) gene can cause the autosomal dominant disorder spinocerebellar ataxia type 2 (SCA2) as well as increase the risk of ALS. Abnormal molecular, motor, and neurophysiological phenotypes in SCA2 mouse models are normalized by lowering ATXN2 transcription, and reduction of nonmutant Atxn2 expression has been shown to increase the life span of mice overexpressing the TDP-43 (transactive response DNA-binding protein 43 kDa) ALS protein, demonstrating the potential benefits of targeting ATXN2 transcription in humans. Here, we describe a quantitative high-throughput screen to identify compounds that lower ATXN2 transcription. We screened 428,759 compounds in a multiplexed assay using an ATXN2-luciferase reporter in human embryonic kidney 293 (HEK-293) cells and identified a diverse set of compounds capable of lowering ATXN2 transcription. We observed dose-dependent reductions of endogenous ATXN2 in HEK-293 cells treated with procillaridin A, 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG), and heat shock protein 990 (HSP990), known inhibitors of HSP90 and Na+/K+-ATPases. Furthermore, HEK-293 cells expressing polyglutamine-expanded ATXN2-Q58 treated with 17-DMAG had minimally detectable ATXN2, as well as normalized markers of autophagy and endoplasmic reticulum stress, including STAU1 (Staufen 1), molecular target of rapamycin, p62, LC3-II (microtubule-associated protein 1A/1B-light chain 3II), CHOP (C/EBP homologous protein), and phospho-eIF2α (eukaryotic initiation factor 2α). Finally, bacterial artificial chromosome ATXN2-Q22 mice treated with 17-DMAG or HSP990 exhibited highly reduced ATXN2 protein abundance in the cerebellum. Taken together, our study demonstrates inhibition of HSP90 or Na+/K+-ATPases as potentially effective therapeutic strategies for treating SCA2 and ALS.

Identification of Selective CYP3A7 and CYP3A4 Substrates and Inhibitors Using a High-Throughput Screening Platform

Cytochrome P450 (CYP) 3A7 is one of the major xenobiotic metabolizing enzymes in human embryonic, fetal, and newborn liver. CYP3A7 expression has also been observed in a subset of the adult population, including pregnant women, as well as in various cancer patients. The characterization of CYP3A7 is not as extensive as other CYPs, and health authorities have yet to provide guidance towards DDI assessment. To identify potential CYP3A7-specific molecules, we used a P450-Glo CYP3A7 enzyme assay to screen a library of ∼5,000 compounds, including FDA-approved drugs and drug-like molecules, and compared these screening data with that from a P450-Glo CYP3A4 assay. Additionally, a subset of 1,000 randomly selected compounds were tested in a metabolic stability assay. By combining the data from the qHTS P450-Glo and metabolic stability assays, we identified several chemical features important for CYP3A7 selectivity. Halometasone was chosen for further evaluation as a potential CYP3A7-selective inhibitor using molecular docking. From the metabolic stability assay, we identified twenty-two CYP3A7-selective substrates over CYP3A4 in supersome setting. Our data shows that CYP3A7 has ligand promiscuity, much like CYP3A4. Furthermore, we have established a large, high-quality dataset that can be used in predictive modeling for future drug metabolism and interaction studies.

Natural products for infectious microbes and diseases: an overview of sources, compounds, and chemical diversities

As coronavirus disease 2019 (COVID-19) threatens human health globally, infectious disorders have become one of the most challenging problem for the medical community. Natural products (NP) have been a prolific source of antimicrobial agents with widely divergent structures and a range vast biological activities. A dataset comprising 618 articles, including 646 NP-based compounds from 672 species of natural sources with biological activities against 21 infectious pathogens from five categories, was assembled through manual selection of published articles. These data were used to identify 268 NP-based compounds classified into ten groups, which were used for network pharmacology analysis to capture the most promising lead-compounds such as agelasine D, dicumarol, dihydroartemisinin and pyridomycin. The distribution of maximum Tanimoto scores indicated that compounds which inhibited parasites exhibited low diversity, whereas the chemistries inhibiting bacteria, fungi, and viruses showed more structural diversity. A total of 331 species of medicinal plants with compounds exhibiting antimicrobial activities were selected to classify the family sources. The family Asteraceae possesses various compounds against C. neoformans, the family Anacardiaceae has compounds against Salmonella typhi, the family Cucurbitacea against the human immunodeficiency virus (HIV), and the family Ancistrocladaceae against Plasmodium. This review summarizes currently available data on NP-based antimicrobials against refractory infections to provide information for further discovery of drugs and synthetic strategies for anti-infectious agents.

Repurposing drugs as COVID-19 therapies: a toxicity evaluation

Drug repurposing is an appealing method to address the Coronavirus 2019 (COVID-19) pandemic because of the low cost and efficiency. We analyzed our in-house database of approved drug screens and compared their activity profiles with results from a severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) cytopathic effect (CPE) assay. The activity profiles of the human ether-à-go-go-related gene (hERG), phospholipidosis (PLD), and many cytotoxicity screens were found significantly correlated with anti-SARS-CoV-2 activity. hERG inhibition is a nonspecific off-target effect that has contributed to promiscuous drug interactions, whereas drug-induced PLD is an undesirable effect linked to hERG blockers. Thus, this study identifies preferred drug candidates as well as chemical structures that should be avoided because of their potential to induce toxicity. Lastly, we highlight the hERG liability of anti-SARS-CoV-2 drugs currently enrolled in clinical trials.

A High-Throughput Screening Assay to Identify Drugs that Can Treat Long QT Syndrome Caused by Trafficking-Deficient KV11.1 (hERG) Variants

Loss-of-function (LOF) variants in the KV11.1 potassium channel cause long QT syndrome (LQTS). Most variants disrupt intracellular channel transport (trafficking) to the cell membrane. Since some channel inhibitors improve trafficking of KV11.1 variants, a high-throughput screening (HTS) assay to detect trafficking enhancement would be valuable to the identification of drug candidates. The thallium (Tl+) flux assay technique, widely used for drug screening, was optimized using human embryonic kidney (HEK-293) cells expressing a trafficking-deficient KV11.1 variant in 384-well plates. Assay quality was assessed using Z prime (Z') scores comparing vehicle to E-4031, a drug that increases KV11.1 membrane trafficking. The optimized assay was validated by immunoblot, electrophysiology experiments, and a pilot drug screen. The combination of: 1) truncating the trafficking-deficient variant KV11.1-G601S (KV11.1-G601S-G965*X) with the addition of 2) KV11.1 channel activator (VU0405601) and 3) cesium (Cs+) to the Tl+ flux assay buffer resulted in an outstanding Z' of 0.83. To validate the optimized trafficking assay, we carried out a pilot screen that identified three drugs (ibutilide, azaperone, and azelastine) that increase KV11.1 trafficking. The new assay exhibited 100% sensitivity and specificity. Immunoblot and voltage-clamp experiments confirmed that all three drugs identified by the new assay improved membrane trafficking of two additional LQTS KV11.1 variants. We report two new ways to increase target-specific activity in trafficking assays-genetic modification and channel activation-that yielded a novel HTS assay for identifying drugs that improve membrane expression of pathogenic KV11.1 variants. SIGNIFICANCE STATEMENT: This manuscript reports the development of a high-throughput assay (thallium flux) to identify drugs that can increase function in KV11.1 variants that are trafficking-deficient. Two key aspects that improved the resolving power of the assay and could be transferable to other ion channel trafficking-related assays include genetic modification and channel activation.

The druggable schizophrenia genome: from repurposing opportunities to unexplored drug targets

There have been no new drugs for the treatment of schizophrenia in several decades and treatment resistance represents a major unmet clinical need. The drugs that exist are based on serendipitous clinical observations rather than an evidence-based understanding of disease pathophysiology. In the present review, we address these bottlenecks by integrating common, rare, and expression-related schizophrenia risk genes with knowledge of the druggability of the human genome as a whole. We highlight novel drug repurposing opportunities, clinical trial candidates which are supported by genetic evidence, and unexplored therapeutic opportunities in the lesser-known regions of the schizophrenia genome. By identifying translational gaps and opportunities across the schizophrenia disease space, we discuss a framework for translating increasingly well-powered genetic association studies into personalized treatments for schizophrenia and initiating the vital task of characterizing clinically relevant drug targets in underexplored regions of the human genome.

Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling

Identification of anti-SARS-CoV-2 compounds through traditional high-throughput screening (HTS) assays is limited by high costs and low hit rates. To address these challenges, we developed machine learning models to identify compounds acting via inhibition of the entry of SARS-CoV-2 into human host cells or the SARS-CoV-2 3-chymotrypsin-like (3CL) protease. The optimal classification models achieved good performance with area under the receiver operating characteristic curve (AUC-ROC) values of >0.78. Experimental validation showed that the best performing models increased the assay hit rate by 2.1-fold for viral entry inhibitors and 10.4-fold for 3CL protease inhibitors compared to those of the original drug repurposing screens. Twenty-two compounds showed potent (<5 μM) antiviral activities in a SARS-CoV-2 live virus assay. In conclusion, machine learning models can be developed and used as a complementary approach to HTS to expand compound screening capacities and improve the speed and efficiency of anti-SARS-CoV-2 drug discovery.

A gene expression biomarker for predictive toxicology to identify chemical modulators of NF-κB

The nuclear factor-kappa B (NF-κB) is a transcription factor with important roles in inflammation, immune response, and oncogenesis. Dysregulation of NF-κB signaling is associated with inflammation and certain cancers. We developed a gene expression biomarker predictive of NF-κB modulation and used the biomarker to screen a large compendia of gene expression data. The biomarker consists of 108 genes responsive to tumor necrosis factor α in the absence but not the presence of IκB, an inhibitor of NF-κB. Using a set of 450 profiles from cells treated with immunomodulatory factors with known NF-κB activity, the balanced accuracy for prediction of NF-κB activation was > 90%. The biomarker was used to screen a microarray compendium consisting of 12,061 microarray comparisons from human cells exposed to 2,672 individual chemicals to identify chemicals that could cause toxic effects through NF-κB. There were 215 and 49 chemicals that were identified as putative or known NF-κB activators or suppressors, respectively. NF-κB activators were also identified using two high-throughput screening assays; 165 out of the ~3,800 chemicals (ToxCast assay) and 55 out of ~7,500 unique compounds (Tox21 assay) were identified as potential activators. A set of 32 chemicals not previously associated with NF-κB activation and which partially overlapped between the different screens were selected for validation in wild-type and NFKB1-null HeLa cells. Using RT-qPCR and targeted RNA-Seq, 31 of the 32 chemicals were confirmed to be NF-κB activators. These results comprehensively identify a set of chemicals that could cause toxic effects through NF-κB.

Inhibition of mutant RAS-RAF interaction by mimicking structural and dynamic properties of phosphorylated RAS

Undruggability of RAS proteins has necessitated alternative strategies for the development of effective inhibitors. In this respect, phosphorylation has recently come into prominence as this reversible post-translational modification attenuates sensitivity of RAS towards RAF. As such, in this study, we set out to unveil the impact of phosphorylation on dynamics of HRAS^WT and aim to invoke similar behavior in HRAS^G12D mutant by means of small therapeutic molecules. To this end, we performed molecular dynamics (MD) simulations using phosphorylated HRAS and showed that phosphorylation of Y32 distorted Switch I, hence the RAS/RAF interface. Consequently, we targeted Switch I in HRAS^G12D by means of approved therapeutic molecules and showed that the ligands enabled detachment of Switch I from the nucleotide-binding pocket. Moreover, we demonstrated that displacement of Switch I from the nucleotide-binding pocket was energetically more favorable in the presence of the ligand. Importantly, we verified computational findings in vitro where HRAS^G12D/RAF interaction was prevented by the ligand in HEK293T cells that expressed HRAS^G12D mutant protein. Therefore, these findings suggest that targeting Switch I, hence making Y32 accessible might open up new avenues in future drug discovery strategies that target mutant RAS proteins.

Identification of Compounds for Butyrylcholinesterase Inhibition

Butyrylcholinesterase (BChE) is a nonspecific cholinesterase enzyme that hydrolyzes choline-based esters. BChE plays a critical role in maintaining normal cholinergic function like acetylcholinesterase (AChE) through hydrolyzing acetylcholine (ACh). Selective BChE inhibition has been regarded as a viable therapeutic approach in Alzheimer's disease. As of now, a limited number of selective BChE inhibitors are available. To identify BChE inhibitors rapidly and efficiently, we have screened 8998 compounds from several annotated libraries against an enzyme-based BChE inhibition assay in a quantitative high-throughput screening (qHTS) format. From the primary screening, we identified a group of 125 compounds that were further confirmed to inhibit BChE activity, including previously reported BChE inhibitors (e.g., bambuterol and rivastigmine) and potential novel BChE inhibitors (e.g., pancuronium bromide and NNC 756), representing diverse structural classes. These BChE inhibitors were also tested for their selectivity by comparing their IC50 values in BChE and AChE inhibition assays. The binding modes of these compounds were further studied using molecular docking analyses to identify the differences between the interactions of these BChE inhibitors within the active sites of AChE and BChE. Our qHTS approach allowed us to establish a robust and reliable process to screen large compound collections for potential BChE inhibitors.

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence

Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug's representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

SARS-CoV-2-host proteome interactions for antiviral drug discovery

Treatment options for COVID-19, caused by SARS-CoV-2, remain limited. Understanding viral pathogenesis at the molecular level is critical to develop effective therapy. Some recent studies have explored SARS-CoV-2-host interactomes and provided great resources for understanding viral replication. However, host proteins that functionally associate with SARS-CoV-2 are localized in the corresponding subnetwork within the comprehensive human interactome. Therefore, constructing a downstream network including all potential viral receptors, host cell proteases, and cofactors is necessary and should be used as an additional criterion for the validation of critical host machineries used for viral processing. This study applied both affinity purification mass spectrometry (AP-MS) and the complementary proximity-based labeling MS method (BioID-MS) on 29 viral ORFs and 18 host proteins with potential roles in viral replication to map the interactions relevant to viral processing. The analysis yields a list of 693 hub proteins sharing interactions with both viral baits and host baits and revealed their biological significance for SARS-CoV-2. Those hub proteins then served as a rational resource for drug repurposing via a virtual screening approach. The overall process resulted in the suggested repurposing of 59 compounds for 15 protein targets. Furthermore, antiviral effects of some candidate drugs were observed in vitro validation using image-based drug screen with infectious SARS-CoV-2. In addition, our results suggest that the antiviral activity of methotrexate could be associated with its inhibitory effect on specific protein-protein interactions.

NCATS Inxight Drugs: a comprehensive and curated portal for translational research

The United States has a complex regulatory scheme for marketing drugs. Understanding drug regulatory status is a daunting task that requires integrating data from many sources from the United States Food and Drug Administration (FDA), US government publications, and other processes related to drug development. At NCATS, we created Inxight Drugs (https://drugs.ncats.io), a web resource that attempts to address this challenge in a systematic manner. NCATS Inxight Drugs incorporates and unifies a wealth of data, including those supplied by the FDA and from independent public sources. The database offers a substantial amount of manually curated literature data unavailable from other sources. Currently, the database contains 125 036 product ingredients, including 2566 US approved drugs, 6242 marketed drugs, and 9684 investigational drugs. All substances are rigorously defined according to the ISO 11238 standard to comply with existing regulatory standards for unique drug substance identification. A special emphasis was placed on capturing manually curated and referenced data on treatment modalities and semantic relationships between substances. A supplementary resource 'Novel FDA Drug Approvals' features regulatory details of newly approved FDA drugs. The database is regularly updated using NCATS Stitcher data integration tool that automates data aggregation and supports full data access through a RESTful API.