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Inhibitors of histone deacetylases (HDAC) are an important emerging class of drugs for the treatment of cancers. HDAC inhibitors are currently under evaluation in clinical trials as single agents and as sensitizers in combinations with chemotherapies and radiation therapy. Although these drugs have important effects on cancer cell growth and functions, the mechanisms underlying HDAC inhibitor activities remain to be fully defined. By using rational drug design, compound 2, a fluorescent class II HDAC targeting inhibitor, was synthesized and observed to accumulate in the cytoplasmic compartments of treated cells, but not in the nuclei. Furthermore, immunostaining of inhibitor exposed cells for HDAC4 showed accumulation of this enzyme in the cytoplasmic compartment with concomitant increased acetylation of tubulin and nuclear histones. These observations support a mechanism by which nuclear histone acetylation is increased as a result of HDAC4 trapping and sequestration in the cytoplasm after binding to compound 2. The HDAC inhibitor offers potential as a novel theranostic agent, combining diagnostic and therapeutic properties in the same molecule. Mol Cancer Ther; 10(9); 1591–9. ©2011 AACR.

Supplementary Methods, Figures 1-4 from Histone Deacetylase Cytoplasmic Trapping by a Novel Fluorescent HDAC Inhibitor

Supplementary Figure 2 from The Role of Calcium in the Activation of Estrogen Receptor-Alpha

Supplementary Figure 3 from The Role of Calcium in the Activation of Estrogen Receptor-Alpha

Supplementary Legends for Figures 1-5, Methods from The Role of Calcium in the Activation of Estrogen Receptor-Alpha

Supplementary Figure 1 from The Role of Calcium in the Activation of Estrogen Receptor-Alpha

Supplementary Figure 5 from The Role of Calcium in the Activation of Estrogen Receptor-Alpha

Supplementary Figure 4 from The Role of Calcium in the Activation of Estrogen Receptor-Alpha

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

Nonalcoholic steatohepatitis (NASH) is associated with obesity, metabolic syndrome, and dysbiosis of the gut microbiome. Cholecystokinin (CCK) is released by saturated fats and plays an important role in bile acid secretion. CCK receptors are expressed on cholangiocytes, and CCK-B receptor expression increases in the livers of mice with NASH. The farnesoid X receptor (FXR) is involved in bile acid transport and is a target for novel therapeutics for NASH. The aim of this study was to examine the role of proglumide, a CCK receptor inhibitor, in a murine model of NASH and its interaction at FXR. Mice were fed a choline deficient ethionine (CDE) diet to induce NASH. Some CDE-fed mice received proglumide-treated drinking water. Blood was collected and liver tissues were examined histologically. Proglumide’s interaction at FXR was evaluated by computer modeling, a luciferase reporter assay, and tissue FXR expression. Stool microbiome was analyzed by RNA-Sequencing. CDE-fed mice developed NASH and the effect was prevented by proglumide. Computer modeling demonstrated specific binding of proglumide to FXR. Proglumide binding in the reporter assay was consistent with a partial agonist at the FXR with a mean binding affinity of 215 nM. FXR expression was significantly decreased in livers of CDE-fed mice compared to control livers, and proglumide restored FXR expression to normal levels. Proglumide therapy altered the microbiome signature by increasing beneficial and decreasing harmful bacteria. These data highlight the potential novel mechanisms by which proglumide therapy may improve NASH through interaction with the FXR and consequent alteration of the gut microbiome.

Environmental chemical (EC) exposures and our interactions with them has significantly increased in the recent decades. Toxicity associated biological characterization of these chemicals is challenging and inefficient, even with available high-throughput technologies. In this report, we describe a novel computational method for characterizing toxicity, associated biological perturbations and disease outcome, called the Chemo-Phenotypic Based Toxicity Measurement (CPTM). CPTM is used to quantify the EC "toxicity score" (Z

The Omicron (BA.1/B.1.1.529) variant of SARS-CoV-2 harbors an alarming 37 mutations on its spike protein, reducing the efficacy of current COVID-19 vaccines. This study identified CD8+ and CD4+ T cell epitopes from SARS-CoV-2 S protein mutants. To identify the highest quality CD8 and CD4 epitopes from the Omicron variant, we selected epitopes with a high binding affinity towards both MHC I and MHC II molecules and applied other clinical checkpoint predictors including immunogenicity, antigenicity, allergenicity, instability, and toxicity. Subsequently, we found eight Omicron (BA.1/B.1.1.529) specific CD8+ and eleven CD4+ T cell epitopes with a world population coverage of 76.16% and 97.46%, respectively. Additionally, we identified common epitopes across Omicron BA.1 and BA.2 lineages that target mutations critical to SARS-CoV-2 virulence. Further, we identified common epitopes across B.1.1.529 and other circulating SARS-CoV-2 variants, such as B.1.617.2 (Delta). We predicted CD8 epitopes’ binding affinity to murine MHC alleles to test the vaccine candidates in preclinical models. The CD8 epitopes were further validated using our previously developed software tool PCOptim. We then modeled the three-dimensional structures of our top CD8 epitopes to investigate the binding interaction between peptide-MHC and peptide-MHC-TCR complexes. Importantly, our identified epitopes are targeting the mutations on the RNA-binding domain and the fusion sites of S protein. This could potentially eliminate viral infections and form long-term immune responses compared to rather short-lived mRNA vaccines and maximize the efficacy of vaccine candidates against the current pandemic and potential future variants.

Electrostatic interactions drive biomolecular interactions and associations. Computational modeling of electrostatics in biomolecular systems, such as protein-ligand, protein–protein, and protein-DNA, has provided atomistic insights into the binding process. In drug discovery, finding biologically plausible ligand-protein target interactions is challenging as current virtual screening and adjuvant techniques such as docking methods do not provide optimal treatment of electrostatic interactions. This study describes a novel electrostatics-driven virtual screening method called ‘ES-Screen’ that performs well across diverse protein target systems. ES-Screen provides a unique treatment of electrostatic interaction energies independent of total electrostatic free energy, typically employed by current software. Importantly, ES-Screen uses initial ligand pose input obtained from a receptor-based pharmacophore, thus independent of molecular docking. ES-Screen integrates individual polar and nonpolar replacement energies, which are the energy costs of replacing the cognate ligand for a target with a query ligand from the screening. This uniquely optimizes thermodynamic stability in electrostatic and nonpolar interactions relative to an experimentally determined stable binding state. ES-Screen also integrates chemometrics through shape and other physicochemical properties to prioritize query ligands with the greatest physicochemical similarities to the cognate ligand. The applicability of ES-Screen is demonstrated with in vitro experiments by identifying novel targets for many drugs. The present version includes a combination of many other descriptor components that, in a future version, will be purely based on electrostatics. Therefore, ES-Screen is a first-in-class unique electrostatics-driven virtual screening method with a unique implementation of replacement electrostatic interaction energies with broad applicability in drug discovery.

Accurately predicting ligand binding affinity in a virtual screening campaign is still challenging. Here, we developed hybrid neural network (HNN) machine deep learning methods, HNN-denovo and HNN-affinity, by combining the 3D-CNN (convolutional neural network) and the FFNN (fast forward neural network) hybrid neural network framework. The HNN-denovo uses protein pocket structure and protein–ligand interactions as input features. The HNN-affinity uses protein sequences and ligand features as input features. The HNN method combines the CNN and FCNN machine architecture for the protein structure or protein sequence and ligand descriptors. To train the model, the HNN methods used thousands of known protein–ligand binding affinity data retrieved from the PDBBind database. We also developed the Random Forest (RF), Gradient Boosting (GB), Decision Tree with AdaBoost (DT), and a consensus model. We compared the HNN results with models developed based on the RF, GB, and DT methods. We also independently compared the HNN method results with the literature reported deep learning protein–ligand binding affinity predictions made by the DLSCORE, KDEEP, and DeepAtom. The predictive performance of the HNN methods (max Pearson’s R achieved was 0.86) was consistently better than or comparable to the DLSCORE, KDEEP, and DeepAtom deep learning learning methods for both balanced and unbalanced data sets. The HNN-affinity can be applied for the protein–ligand affinity prediction even in the absence of protein structure information, as it considers the protein sequence as standalone feature in addition to the ligand descriptors. The HNN-denovo method can be efficiently implemented to the structure-based de novo drug design campaign. The HNN-affinity method can be used in conjunction with the deep learning molecular docking protocols as a standalone. Further, it can be combined with the conventional molecular docking methods as a multistep approach to rapidly screen billions of diverse compounds. The HNN method are highly scalable in the cloud ML platform.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, has challenged public health at an unprecedented scale which has led to a dramatic loss of human life worldwide. To design a protective vaccine against SARS-CoV-2, it is necessary to understand which SARS-CoV-2 specific epitopes can elicit a T cell response and provide protection across a broad population. In this study, PLpro and RdRp, two immunogenic non-structural proteins from an immunodominant gene region ORF1ab, as well as ORF3a and ORF9b are identified as potential vaccine targets against SARS-CoV-2. To select top epitopes for vaccine design, we used various clinical properties, such as antigenicity, allergenicity, toxicity and IFN-y secretion. The analysis of CD8 and CD4 T cell epitopes revealed multiple potential vaccine constructs that cover a high percentage of the world population. We identified 8 immunogenic, antigenic, non-allergenic, non-toxic, stable and IFN-y inducing CD8 proteins for nsp3, 4 for nsp12, 11 for ORF3a and 3 for ORF9b that are common across four lineages of variants of concern – B.1.1.7, P.1, B.1.351 and B.1.617.2, which protect 98.12%, 87.08%, 96.07% and 63.8% of the world population, respectively. We also identified variant specific T cell epitopes that could be useful in targeting each variant strain separately. Including the prediction of mouse MHC affinity towards our top CD8 epitopes, our study revealed a total of 3 immunogenic, antigenic, non-allergenic, non-toxic, stable and IFN-y inducing CD8 epitopes overlapping with 6 antigenic, non-allergenic, non-toxic, stable and IFN-y inducing CD4 epitopes across all four variants of concern which can effectively be utilized in pre-clinical studies. The landscape of SARS-CoV-2 T cell epitopes that we identified can help lead SARS-CoV-2 vaccine development as well as epitope-based peptide vaccine research in the future.

The current COVID-19 pandemic continues to spread and devastate in the absence of effective treatments, warranting global concern and action. Despite progress in vaccine development, the rise of novel, increasingly infectious SARS-CoV-2 variants makes it clear that our response to the virus must continue to evolve along with it. The use of immunoinformatics provides an opportunity to rapidly and efficiently expand the tools at our disposal to combat the current pandemic and prepare for future outbreaks through epitope-based vaccine design. In this study, we validated and compared the currently available epitope prediction tools, and then used the best tools to predict T cell epitopes from SARS-CoV-2 spike and nucleocapsid proteins for use in an epitope-based vaccine. We combined the mouse MHC affinity predictor and clinical predictors such as HLA affinity, immunogenicity, antigenicity, allergenicity, toxicity and stability to select the highest quality CD8 and CD4 T cell epitopes for the common SARS-CoV-2 variants of concern suitable for further preclinical studies. We also identified variant-specific epitopes to more precisely target the Alpha, Beta, Gamma, Delta, Cluster 5 and US variants. We then modeled the 3D structures of our top 4 N and S epitopes to investigate the molecular interaction between peptide-MHC and peptide-MHC-TCR complexes. Following in vitro and in vivo validation, the epitopes identified by this study may be used in an epitope-based vaccine to protect across all current variants, as well as in variant-specific booster shots to target variants of concern. Immunoinformatics tools allowed us to efficiently predict epitopes in silico most likely to prove effective in vivo, providing a more streamlined process for vaccine development in the context of a rapidly evolving pandemic.

Due to the small number of molecular targets in viruses and the rapid evolution of viral genes, it is very challenging to develop specific antiviral drugs. Viruses require host factors to translate their transcripts, and targeting the host factor(s) offers a unique opportunity to develop broad antiviral drugs. It is well documented that some viruses utilize a host protein, Receptor for Activated C Kinase 1 (RACK1), to translate their mRNAs using a viral mRNA secondary structure known as the Internal Ribosomal Entry Site (IRES). RACK1 is essential for the translation of many viruses including hepatitis C (HCV), polio, Drosophila C (DCV), Dengue, Cricket Paralysis (CrpV), and vaccinia viruses. In addition, HIV-1 and Herpes Simplex virus (HSV-1) are known to use IRES as well. Therefore, host RACK1 protein is an attractive target for developing broad antiviral drugs. Depletion of the host’s RACK1 will potentially inhibit virus replication. This background study has led us to the development of novel antiviral therapeutics, such as RACK1 inhibitors. By utilizing the crystal structure of the RACK1A protein from the model plant Arabidopsis and using a structure based drug design method, dozens of small compounds were identified that could potentially bind to the experimentally determined functional site of the RACK1A protein. The SPR assays showed that the small compounds bound strongly to recombinant RACK1A protein. Here we provide evidence that the drugs show high efficacy in inhibition of HSV-1 proliferation in a HEp-2 cell line. The drug showed similar efficacy as the available anti-herpes drug acyclovir and showed supralinear effect when applied in a combinatorial manner. As an increasing number of viruses are reported to use host RACK1 proteins, and more than 100 diverse animals and plant disease-causing viruses are known to use IRES-based translation, these drugs can be established as host-targeted broad antiviral drugs.

Scaffold protein RACK1 mediates cancer cell migration mostly through regulation of focal adhesion (FA) assembly by promoting a focal adhesion kinase (FAK) activation downstream of the integrin clustering and adhesion at the extracellular matrix (ECM). Here we demonstrated the efficacy of our recently developed RACK1 Y246 phosphorylation inhibitor compounds (SD29 and SD29-14) to inhibit the migration and invasion of MCF7 and MDA-MB-231 breast cancer cell lines. Using multiple assays, our results confirmed that inhibitor compounds effectively prevent the filopodia/lamellipodia development and inhibits the migration of breast cancer cells. A mechanistic model of the inhibitor compounds has been developed. Migration and invasion capabilities of the cancer cells define the metastasis of cancer. Thus, our results suggest a potential therapeutic mechanism of the inhibitors to prevent metastasis in diverse cancers.

Determining environmental chemical carcinogenicity is an urgent need as humans are increasingly exposed to these chemicals. In this study, we determined the carcinogenicity of wide variety real-life exposure chemicals in large scale. To determine chemical carcinogenicity, we have developed carcinogenicity prediction models based on the hybrid neural network (HNN) architecture. In the HNN model, we included new SMILES feature representation method, by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We used 653 molecular descriptors modeled by feed forward neural network (FFNN), and SMILES as chemical features to train the models. We have developed three types of machine learning models: binary classification models to predict chemical is a carcinogenic or non-carcinogenic, multiclass classification models to predict severity of the chemical carcinogenicity, and regression models to predict median toxic dose of the chemicals. Along with the hybrid neural network (HNN) model that we developed, Random Forest (RF), Bootstrap Aggregating (Bagging) and Adaptive Boosting (AdaBoost) methods were also used for binary and multiclass classification. Regression models were developed using HNN, RF, Support Vector Regressor (SVR), Gradient Boosting (GB), Kernel Ridge (KR), Decision Tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. For binary classification, our HNN model predicted with an average accuracy of 74.33% and an average AUC of 0.806, for multiclass classification, the HNN model predicted with an average accuracy of 50.58% and an average micro-AUC of 0.68, and for regression model, the consensus method achieved R2 of 0.40. The predictive performance of our models based on a highly diverse chemicals is comparable to the literature reported models that included the similar and less diverse molecules. Our models can be used in identifying the potentially carcinogenic chemicals for a wide variety of chemical classes.

Host ribosome-associated scaffold protein Receptor for Activated C Kinase 1 (RACK1) is utilized by a diverse group of human viruses for Internal Ribosomal Entry Sites (IRES) – mediated translation of viral mRNAs. We recently reported inhibition of herpes virus by small molecules targeting the RACK1 functional site. Here, we tested these molecules against HIV-1 and HCV, as HIV-1 contains two potential IRES sites and HCV translation occurs exclusively through IRES. Compounds significantly downregulated activities of HIV-1- and HCV-related dicistronic reporter constructs in transfected HEK293T cells. The compounds also strongly downregulated production of the HIV-1 capsid protein p24 in HIV-infected cells, as well as production of HIV-1 Gag precursor p55 and p55-derived proteins p24 and p17 in cells infected with the HIV-1 virus. Hepatitis C virus (HCV) IRES activities were also significantly inhibited by RACK1 inhibitor compounds. Since a number of human and plant pathogenic viruses are reported to use IRES, the RACK1 compounds can be established as broad host-targeted antivirals.

Given the rapid spread of SARS-CoV-2 and rising death toll of COVID-19 in the current absence of effective treatments, it is imperative that therapeutics are developed and made available to patients as quickly as possible. Publicly available COVID-19 patient data can be used to identify host therapeutic targets, tailoring treatments to the disease signatures observed in patients. In this study, we identify potential host therapeutic targets based on gene expression alterations observed in COVID-19 patients. We analyzed RNAseq data from airway samples of COVID-19 patients and healthy controls to detect significantly differentially expressed genes and pathways that present potential therapeutic targets. Our analysis revealed expression changes in key genes involved in activation of immune pathways, as well as genes targeted by SARS-CoV2 to interfere with normal host cell functioning. Critical changes were observed in a number of genes, including EIF2AK2, which was shown to play important roles in activating the interferon response and interfering with host cell translational machinery in SARS-CoV-2 infection, presenting a prospective therapeutic target. We also identified drugs with potential to modulate multiple therapeutic targets within the most significant pathways. Our results both validate key genes, pathways, and drug candidates that have been reported by other studies and suggest others that have not been well-characterized and warrant further investigation by future studies. Further investigation of these therapeutic targets and their drug interactions may lead to effective therapeutic strategies to combat the current COVID-19 pandemic and protect against future outbreaks.

Humans are exposed to thousands of potentially toxic chemicals including environmental chemicals such as industrial wastes, food products, solvents, air pollutants, fertilizers, pesticides, insecticides, carcinogens, drugs, metals/metalloids, and other industrial chemicals. Approximately 300,000 such chemicals currently in use, unfortunately little is known about their potential toxicity. Determining human toxicity potential of chemicals remains a challenge due to a substantial resource required to assess a chemical in-vivo, and only a few thousand single chemicals in commercial use has been evaluated. In this study, to predict the environmental chemical toxicity, we developed a new hybrid neural network (HNN) deep learning model consisting of a Convolutional Neural Network (CNN) and multilayer perceptron (MLP) type feed forward neural network (FFNN). Our HNN deep learning model trained based on thousands of chemicals, presented the best performance for majority of the cases. Taken together, our hybrid HNN deep learning models has a wide applicability in the prediction of toxicity of any chemical category and its mixtures.

Humans are exposed to thousands of potentially toxic chemicals including environmental chemicals such as industrial wastes, food products, solvents, air pollutants, fertilizers, pesticides, insecticides, carcinogens, drugs, metals/metalloids, and other industrial chemicals. Approximately 300,000 such chemicals currently in use, unfortunately little is known about their potential toxicity. Determining human toxicity potential of chemicals remains a challenge due to a substantial resource required to assess a chemical in-vivo, and only a few thousand single chemicals in commercial use has been evaluated. In this study, to predict the environmental chemical toxicity, we developed a new hybrid neural network (HNN) deep learning model consisting of a Convolutional Neural Network (CNN) and multilayer perceptron (MLP) type feed forward neural network (FFNN). Our HNN deep learning model trained based on thousands of chemicals, presented the best performance for majority of the cases. Taken together, our hybrid HNN deep learning models has a wide applicability in the prediction of toxicity of any chemical category and its mixtures.

Among the genetic and environmental contributors to dilated cardiomyopathy (DCM), the pathogenesis of drug-induced DCM is heterogeneous, poorly understood, and difficult to predict. In this study, transcriptomic data from two recently published datasets were used to identify genes which are differentially expressed in DCM compared to cardiac tissue from non-failing donors. DCM-associated genes were then mapped using gene set enrichment analysis to associated biochemical pathways, gene ontology functions, and protein-protein interactions, creating a DCM-associated biological perturbation network. Corresponding multi-scale pharmacologic activity networks for over 9000 drugs were produced using public repositories of known drug-protein interactions. Drugs were then rank-ordered based on multi-scale statistical significance of concordance with the DCM perturbation network while accounting for direction of effect using a causal analysis algorithm. A predictive algorithm using overrepresentation analysis was developed using the SIDER database of adverse drug reactions (ADRs) to formulate ADR-protein and ADR-pathway associations, which were then compared to the drug-DCM association networks produced by our model. In addition to a demonstrated concordance between the rank lists for each dataset, this platform prioritized multiple drugs and metabolites which are known to be associated with DCM, such as doxorubicin, risperidone, and 4-hydroxynonenal, as well as associated mechanisms such as bile acid excess and inhibition of DNA topoisomerase II and mitochondrial complex III. Multi-scale concordance of drug and DCM networks highlighted the involvement of heat shock proteins and associated angiotensin II activity, supporting current evidence that its inhibition may be cardioprotective to drug-induced DCM. Our approach also recapitulated the roles of drugs, genes, and pathways that hold prognostic and therapeutic significance for DCM, such as the abilities of sulforaphane and fenofibrate to prevent DCM in animal models of type 2 diabetes. This platform can be used to predict a wide array of adverse drug reactions and their underlying mechanisms based on bioactivity data.

Immunomodulatory drugs (IMiDs) are important for the treatment of multiple myeloma and myelodysplastic syndrome. Binding of IMiDs to Cereblon (CRBN), the substrate receptor of the CRL4CRBN E3 ubiquitin ligase, induces cancer cell death by targeting key neo-substrates for degradation. Despite this clinical significance, the physiological regulation of CRBN remains largely unknown. Herein we demonstrate that Wnt, the extracellular ligand of an essential signal transduction pathway, promotes the CRBN-dependent degradation of a subset of proteins. These substrates include Casein kinase 1α (CK1α), a negative regulator of Wnt signaling that functions as a key component of the β-Catenin destruction complex. Wnt stimulation induces the interaction of CRBN with CK1α and its resultant ubiquitination, and in contrast with previous reports does so in the absence of an IMiD. Mechanistically, the destruction complex is critical in maintaining CK1α stability in the absence of Wnt, and in recruiting CRBN to target CK1α for degradation in response to Wnt. CRBN is required for physiological Wnt signaling, as modulation of CRBN in zebrafish and Drosophila yields Wnt-driven phenotypes. These studies demonstrate an IMiD-independent, Wnt-driven mechanism of CRBN regulation and provide a means of controlling Wnt pathway activity by CRBN, with relevance for development and disease., Cereblon (CRBN) is an E3 ubiquitin ligase substrate receptor that is involved in cancer cell death, although its regulation is poorly understood. Here, the authors show that Wnt ligand increases CRBN-dependent protein degradation and demonstrate CRBN’s importance in physiological Wnt signaling.

Background Drug repurposing enables the discovery of potential cancer treatments using publically available data from over 4000 published Food and Drug Administration approved and experimental drugs. However, the ability to effectively evaluate the drug's efficacy remains a challenge. Impediments to broad applicability include inaccuracies in many of the computational drug‐target algorithms and a lack of clinically relevant biologic modeling systems to validate the computational data for subsequent translation. Methods We have integrated our computational proteochemometric systems network pharmacology platform, DrugGenEx‐Net, with primary, continuous cultures of conditionally reprogrammed (CR) normal and prostate cancer (PCa) cells derived from treatment‐naive patients with primary PCa. Results Using the transcriptomic data from two matched pairs of benign and tumor‐derived CR cells, we constructed drug networks to describe the biological perturbation associated with each prostate cell subtype at multiple levels of biological action. We prioritized the drugs by analyzing these networks for statistical coincidence with the drug action networks originating from known and predicted drug‐protein targets. Prioritized drugs shared between the two patients’ PCa cells included carfilzomib (CFZ), bortezomib (BTZ), sulforaphane, and phenethyl isothiocyanate. The effects of these compounds were then tested in the CR cells, in vitro. We observed that the IC50 values of the normal PCa CR cells for CFZ and BTZ were higher than their matched tumor CR cells. Transcriptomic analysis of CFZ‐treated CR cells revealed that genes involved in cell proliferation, proteases, and downstream targets of serine proteases were inhibited while KLK7 and KLK8 were induced in the tumor‐derived CR cells. Conclusions Given that the drugs in the database are extremely well‐characterized and that the patient‐derived cells are easily scalable for high throughput drug screening, this combined in vitro and in silico approach may significantly advance personalized PCa treatment and for other cancer applications.

The SARS-CoV-2 virus’s ability to induce hypercytokinemia and cause multiple organ failure makes it imperative to find effective treatments. To understand the mechanism of viral infection and its effects on organ tissues, we analyzed multiple single-cell and bulk RNAseq data from COVID-19 patients’ organ samples. Various levels of severity of infection were accounted for, with comparative analyses between mild, moderate, and severely infected patients. Our analysis uncovered an upregulation of the innate immune response via several inflammatory genes, IL-2, IL-6, IL-8, IL-17A, and NF-κB. Consequently, we found that the upregulation of these downstream effects can lead to organ injury. The downregulated pathways such as eukaryotic initiation factor 2 (eIF2) and eIF4-mediated host translation, were found to lead to an increased viral translation. We also found that the loss of inhibitory peptides can suppress an overactive innate immune response via NF-κB and interleukin-mediated pathways. Investigation of viral-host protein mapping showed that the interaction of viral proteins with host proteins correlated with the down- and upregulation of host pathways such as decreased eIF2-mediated host translation and increased hypertrophy and fibrosis. Inflammation was increased via the stimulation of pro-inflammatory cytokines and suppression of host translation pathways that led to reduced inflammatory inhibitors. Cardiac hypertrophy and organ fibrosis were the results of increased inflammation in organs of severe and critical patients. Finally, we identified potential therapeutic targets for the treatment of COVID-19 and its deleterious effects on organs. Further experimental investigation would conclusively determine the effects of COVID-19 infection on organs other than the lungs and the effectiveness of the proposed therapeutic targets.

The spread of SARS-CoV-2 and the increasing mortality rates of COVID-19 create an urgent need for treatments, which are currently lacking. Although vaccines have been approved by the FDA for emergency use in the U.S., patients will continue to require pharmacologic intervention to reduce morbidity and mortality as vaccine availability remains limited. The rise of new variants makes the development of therapeutic strategies even more crucial to combat the current pandemic and future outbreaks. Evidence from several studies suggests the host immune response to SARS-CoV-2 infection plays a critical role in disease pathogenesis. Consequently, host immune factors are becoming more recognized as potential biomarkers and therapeutic targets for COVID-19. To develop therapeutic strategies to combat current and future coronavirus outbreaks, understanding how the coronavirus hijacks the host immune system during and after the infection is crucial. In this study, we investigated immunological patterns or characteristics of the host immune response to SARS-CoV-2 infection that may contribute to the disease severity of COVID-19 patients. We analyzed large bulk RNASeq and single cell RNAseq data from COVID-19 patient samples to immunoprofile differentially expressed gene sets and analyzed pathways to identify human host protein targets. We observed an immunological profile of severe COVID-19 patients characterized by upregulated cytokines, interferon-induced proteins, and pronounced T cell lymphopenia, supporting findings by previous studies. We identified a number of host immune targets including PERK, PKR, TNF, NF-kB, and other key genes that modulate the significant pathways and genes identified in COVID-19 patients. Finally, we identified genes modulated by COVID-19 infection that are implicated in oncogenesis, including E2F transcription factors and RB1, suggesting a mechanism by which SARS-CoV-2 infection may contribute to oncogenesis. Further clinical investigation of these targets may lead to bonafide therapeutic strategies to treat the current COVID-19 pandemic and protect against future outbreaks and viral escape variants.

Triple negative breast cancer (TNBC) is a group of cancers whose heterogeneity and shortage of effective drug therapies has prompted efforts to divide these cancers into molecular subtypes. Our computational platform, entitled GenEx-TNBC, applies concepts in systems biology and polypharmacology to prioritize thousands of approved and experimental drugs for therapeutic potential against each molecular subtype of TNBC. Using patient-based and cell line-based gene expression data, we constructed networks to describe the biological perturbation associated with each TNBC subtype at multiple levels of biological action. These networks were analyzed for statistical coincidence with drug action networks stemming from known drug-protein targets, while accounting for the direction of disease modulation for coinciding entities. GenEx-TNBC successfully designated drugs, and drug classes, that were previously shown to be broadly effective or subtype-specific against TNBC, as well as novel agents. We further performed biological validation of the platform by testing the relative sensitivities of three cell lines, representing three distinct TNBC subtypes, to several small molecules according to the degree of predicted biological coincidence with each subtype. GenEx-TNBC is the first computational platform to associate drugs to diseases based on inverse relationships with multi-scale disease mechanisms mapped from global gene expression of a disease. This method may be useful for directing current efforts in preclinical drug development surrounding TNBC, and may offer insights into the targetable mechanisms of each TNBC subtype.

// Cynthia M. Simbulan-Rosenthal 1,* , Sivanesan Dakshanamurthy 1,2,* , Anirudh Gaur 1 , You-Shin Chen 1 , Hong-Bin Fang 3 , Maryam Abdussamad 4 , Hengbo Zhou 4 , John Zapas 4 , Valerie Calvert 5 , Emanuel F. Petricoin 5 , Michael B. Atkins 2 , Stephen W. Byers 1,2 and Dean S. Rosenthal 1 1 Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, USA 2 Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA 3 Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA 4 MedStar Franklin Square Medical Center, Baltimore, MD, USA 5 Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA * These authors are co-first authors Correspondence to: Dean S. Rosenthal, email: // Keywords : mebendazole, drug repurposing, melanoma, ERK pathway, BRAF Received : March 31, 2016 Accepted : October 13, 2016 Published : February 02, 2017 Abstract Structure-based drug repositioning in addition to random chemical screening is now a viable route to rapid drug development. Proteochemometric computational methods coupled with kinase assays showed that mebendazole (MBZ) binds and inhibits kinases important in cancer, especially both BRAF WT and BRAF V600E . We find that MBZ synergizes with the MEK inhibitor trametinib to inhibit growth of BRAF WT -NRAS Q61K melanoma cells in culture and in xenografts, and markedly decreased MEK and ERK phosphorylation. Reverse Phase Protein Array (RPPA) and immunoblot analyses show that both trametinib and MBZ inhibit the MAPK pathway, and cluster analysis revealed a protein cluster showing strong MBZ+trametinib - inhibited phosphorylation of MEK and ERK within 10 minutes, and its direct and indirect downstream targets related to stress response and translation, including ElK1 and RSKs within 30 minutes. Downstream ERK targets for cell cycle, including cMYC, were down-regulated, consistent with S- phase suppression by MBZ+trametinib, while apoptosis markers, including cleaved caspase-3, cleaved PARP and a sub-G1 population, were all increased with time. These data suggest that MBZ, a well-tolerated off-patent approved drug, should be considered as a therapeutic option in combination with trametinib, for patients with NRAS Q61mut or other non-V600E BRAF mutant melanomas.

BACKGROUND & AIMS: Pancreatic ductal adenocarcinomas (PDAC) are characterized by fibrosis and an abundance of cancer-associated fibroblasts (CAFs). We investigated strategies to disrupt interactions among CAFs, the immune system, and cancer cells, focusing on adhesion molecule cadherin 11 (CDH11), which has been associated with other fibrotic disorders and is expressed by activated fibroblasts. METHODS: We compared levels of CDH11 mRNA in human pancreatitis and pancreatic cancer tissues and cells, compared with normal pancreas, and measured levels of CDH11 protein in human and mouse pancreatic lesions and normal tissues. We crossed p48-Cre;LSL-Kras(G12D/+);LSL-Trp53(R172H/+) (KPC) mice with CDH11-knockout mice and measured survival times of offspring. Pancreata were collected and analyzed by histology, immunohistochemistry, and (single-cell) RNA sequencing; RNA and proteins were identified by imaging mass cytometry. Some mice were given injections of PD1 antibody or gemcitabine and survival was monitored. Pancreatic cancer cells from KPC mice were subcutaneously injected into Cdh11(+/+) and Cdh11(−/−) mice and tumor growth was monitored. Pancreatic cancer cells (mT3) from KPC mice (C57BL/6), were subcutaneously injected into Cdh11(+/+) (C57BL/6J) mice and mice were given injections of antibody against CDH11, gemcitabine, or small molecule inhibitor of CDH11 (SD133) and tumor growth was monitored. RESULTS: Levels of CDH11 mRNA and protein were significantly higher in CAFs than in pancreatic cancer epithelial cells, human or mouse pancreatic cancer cell lines, or immune cells. KPC/Cdh11(+/−) and KPC/Cdh11(−/−) mice survived significantly longer than KPC/Cdh11(+/+) mice. Markers of stromal activation entirely surrounded pancreatic intraepithelial neoplasias in KPC/Cdh11(+/+) mice and incompletely in KPC/Cdh11(+/−) and KPC/Cdh11(−/−) mice, whose lesions also contained fewer FOXP3(+) cells in the tumor center. Compared with pancreatic tumors in KPC/Cdh11(+/+) mice, tumors of KPC/Cdh11(+/−) mice had increased markers of antigen processing and presentation; more lymphocytes and associated cytokines; decreased extracellular matrix components; and reductions in markers and cytokines associated with immunosuppression. Administration of the PD1 antibody did not prolong survival of KPC mice with 0, 1, or 2 alleles of Cdh11. Gemcitabine extended survival only of KPC/Cdh11(+/−) and KPC/Cdh11(−/−) mice or reduced subcutaneous tumor growth in mT3 engrafted Cdh11(+/+) mice given in combination with the CDH11 antibody. A small molecule inhibitor of CDH11 reduced growth of pre-established mT3 subcutaneous tumors only if T and B cells were present in mice. CONCLUSIONS: Knockout or inhibition of CDH11, which is expressed by CAFs in the pancreatic tumor stroma, reduces growth of pancreatic tumors, increases their response to gemcitabine, and significantly extends survival of mice. CDH11 promotes immunosuppression and extracellular matrix deposition, and might be developed as a therapeutic target for pancreatic cancer.