AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect's vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.

AutoPepVax, a Novel Machine-Learning-Based Program for Vaccine Design: Application to a Pan-Cancer Vaccine Targeting EGFR Missense Mutations

The current epitope selection methods for peptide vaccines often rely on epitope binding affinity predictions, prompting the need for the development of more sophisticated in silico methods to determine immunologically relevant epitopes. Here, we developed AutoPepVax to expedite and improve the in silico epitope selection for peptide vaccine design. AutoPepVax is a novel program that automatically identifies non-toxic and non-allergenic epitopes capable of inducing tumor-infiltrating lymphocytes by considering various epitope characteristics. AutoPepVax employs random forest classification and linear regression machine-learning-based models, which are trained with datasets derived from tumor samples. AutoPepVax, along with documentation on how to run the program, is freely available on GitHub. We used AutoPepVax to design a pan-cancer peptide vaccine targeting epidermal growth factor receptor (EGFR) missense mutations commonly found in lung adenocarcinoma (LUAD), colorectal adenocarcinoma (CRAD), glioblastoma multiforme (GBM), and head and neck squamous cell carcinoma (HNSCC). These mutations have been previously targeted in clinical trials for EGFR-specific peptide vaccines in GBM and LUAD, and they show promise but lack demonstrated clinical efficacy. Using AutoPepVax, our analysis of 96 EGFR mutations identified 368 potential MHC-I-restricted epitope-HLA pairs from 49,113 candidates and 430 potential MHC-II-restricted pairs from 168,669 candidates. Notably, 19 mutations presented viable epitopes for MHC I and II restrictions. To evaluate the potential impact of a pan-cancer vaccine composed of these epitopes, we used our program, PCOptim, to curate a minimal list of epitopes with optimal population coverage. The world population coverage of our list ranged from 81.8% to 98.5% for MHC Class II and Class I epitopes, respectively. From our list of epitopes, we constructed 3D epitope-MHC models for six MHC-I-restricted and four MHC-II-restricted epitopes, demonstrating their epitope binding potential and interaction with T-cell receptors. AutoPepVax's comprehensive approach to in silico epitope selection addresses vaccine safety, efficacy, and broad applicability. Future studies aim to validate the AutoPepVax-designed vaccines with murine tumor models that harbor the studied mutations.

Predicting Dose-Dependent Carcinogenicity of Chemical Mixtures Using a Novel Hybrid Neural Network Framework and Mathematical Approach

This study addresses the challenge of assessing the carcinogenic potential of hazardous chemical mixtures, such as per- and polyfluorinated substances (PFASs), which are known to contribute significantly to cancer development. Here, we propose a novel framework called HNN_MixCancer that utilizes a hybrid neural network (HNN) integrated into a machine-learning framework. This framework incorporates a mathematical model to simulate chemical mixtures, enabling the creation of classification models for binary (carcinogenic or noncarcinogenic) and multiclass classification (categorical carcinogenicity) and regression (carcinogenic potency). Through extensive experimentation, we demonstrate that our HNN model outperforms other methodologies, including random forest, bootstrap aggregating, adaptive boosting, support vector regressor, gradient boosting, kernel ridge, decision tree with AdaBoost, and KNeighbors, achieving a superior accuracy of 92.7% in binary classification. To address the limited availability of experimental data and enrich the training data, we generate an assumption-based virtual library of chemical mixtures using a known carcinogenic and noncarcinogenic single chemical for all the classification models. Remarkably, in this case, all methods achieve accuracies exceeding 98% for binary classification. In external validation tests, our HNN method achieves the highest accuracy of 80.5%. Furthermore, in multiclass classification, the HNN demonstrates an overall accuracy of 96.3%, outperforming RF, Bagging, and AdaBoost, which achieved 91.4%, 91.7%, and 80.2%, respectively. In regression models, HNN, RF, SVR, GB, KR, DT with AdaBoost, and KN achieved average R² values of 0.96, 0.90, 0.77, 0.94, 0.96, 0.96, and 0.97, respectively, showcasing their effectiveness in predicting the concentration at which a chemical mixture becomes carcinogenic. Our method exhibits exceptional predictive power in prioritizing carcinogenic chemical mixtures, even when relying on assumption-based mixtures. This capability is particularly valuable for toxicology studies that lack experimental data on the carcinogenicity and toxicity of chemical mixtures. To our knowledge, this study introduces the first method for predicting the carcinogenic potential of chemical mixtures. The HNN_MixCancer framework offers a novel alternative for dose-dependent carcinogen prediction. Ongoing efforts involve implementing the HNN method to predict mixture toxicity and expanding the application of HNN_MixCancer to include multiple mixtures such as PFAS mixtures and co-occurring chemicals.

A Peptide Vaccine Design Targeting KIT Mutations in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a leading blood cancer subtype that can be caused by 27 gene mutations. Previous studies have explored potential vaccine and drug treatments against AML, but many were proven immunologically insignificant. Here, we targeted this issue and applied various clinical filters to improve immune response. KIT is an oncogenic gene that can cause AML when mutated and is predicted to be a promising vaccine target because of its immunogenic responses when activated. We designed a multi-epitope vaccine targeting mutations in the KIT oncogene using CD8+ and CD4+ epitopes. We selected the most viable vaccine epitopes based on thresholds for percentile rank, immunogenicity, antigenicity, half-life, toxicity, IFNγ release, allergenicity, and stability. The efficacy of data was observed through world and regional population coverage of our vaccine design. Then, we obtained epitopes for optimized population coverage from PCOptim-CD, a modified version of our original Java-based program code PCOptim. Using 24 mutations on the KIT gene, 12 CD8+ epitopes and 21 CD4+ epitopes were obtained. The CD8+ dataset had a 98.55% world population coverage, while the CD4+ dataset had a 65.14% world population coverage. There were five CD4+ epitopes that overlapped with the top CD8+ epitopes. Strong binding to murine MHC molecules was found in four CD8+ and six CD4+ epitopes, demonstrating the feasibility of our results in preclinical murine vaccine trials. We then created three-dimensional (3D) models to visualize epitope-MHC complexes and TCR interactions. The final candidate is a non-toxic and non-allergenic multi-epitope vaccine against KIT mutations that cause AML. Further research would involve murine trials of the vaccine candidates on tumor cells causing AML.

SARS-CoV-2 Omicron (BA.1 and BA.2) specific novel CD8+ and CD4+ T cell epitopes targeting spike protein

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. In this study, we 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. We 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. Notably, 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 relatively short-lived mRNA vaccines and maximize the efficacy of vaccine candidates against the current pandemic and potential future variants.

ES-Screen: A Novel Electrostatics-Driven Method for Drug Discovery Virtual Screening

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.

Predicting Dose-Range Chemical Toxicity using Novel Hybrid Deep Machine-Learning Method

Humans are exposed to thousands of chemicals, including environmental chemicals. Unfortunately, little is known about their potential toxicity, as determining the toxicity remains challenging due to the substantial resources required to assess a chemical in vivo. Here, we present a novel hybrid neural network (HNN) deep learning method, called HNN-Tox, to predict chemical toxicity at different doses. To develop a hybrid HNN-Tox method, we combined two neural network frameworks, the Convolutional Neural Network (CNN) and the multilayer perceptron (MLP)-type feed-forward neural network (FFNN). Combining the CNN and FCNN in the field of environmental chemical toxicity prediction is a novel approach. We developed several binary and multiclass classification models to assess dose-range chemical toxicity that is trained based on thousands of chemicals with known toxicity. The performance of the HNN-Tox was compared with other machine-learning methods, including Random Forest (RF), Bootstrap Aggregation (Bagging), and Adaptive Boosting (AdaBoost). We also analyzed the model performance dependency on varying features, descriptors, dataset size, route of exposure, and toxic dose. The HNN-Tox model, trained on 59,373 chemicals annotated with known LD50 and routes of exposure, maintained its predictive ability with an accuracy of 84.9% and 84.1%, even after reducing the descriptor size from 318 to 51, and the area under the ROC curve (AUC) was 0.89 and 0.88, respectively. Further, we validated the HNN-Tox with several external toxic chemical datasets on a large scale. The HNN-Tox performed optimally or better than the other machine-learning methods for diverse chemicals. This study is the first to report a large-scale prediction of dose-range chemical toxicity with varying features. The HNN-Tox has broad applicability in predicting toxicity for diverse chemicals and could serve as an alternative methodology approach to animal-based toxicity assessment.

A New Hybrid Neural Network Deep Learning Method for Protein-Ligand Binding Affinity Prediction and De Novo Drug Design

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.

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method

Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cancer model, we developed models based on the random forest (RF), bootstrap aggregating (Bagging), and adaptive boosting (AdaBoost) methods for binary and multiclass classification. We developed regression models using HNN-Cancer, RF, support vector regressor (SVR), gradient boosting (GB), kernel ridge (KR), decision tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. The performance of the models for all classifications was assessed using various statistical metrics. The accuracy of the HNN-Cancer, RF, and Bagging models were 74%, and their AUC was ~0.81 for binary classification models developed with 7994 chemicals. The sensitivity was 79.5% and the specificity was 67.3% for the HNN-Cancer, which outperforms the other methods. In the case of multiclass classification models with 1618 chemicals, we obtained the optimal accuracy of 70% with an AUC 0.7 for HNN-Cancer, RF, Bagging, and AdaBoost, respectively. In the case of regression models, the correlation coefficient (R) was around 0.62 for HNN-Cancer and RF higher than the SVM, GB, KR, DTBoost, and NN machine learning methods. Overall, the HNN-Cancer performed better for the majority of the known carcinogen experimental datasets. Further, the predictive performance of HNN-Cancer on diverse chemicals is comparable to the literature-reported models that included similar and less diverse molecules. Our HNN-Cancer could be used in identifying potentially carcinogenic chemicals for a wide variety of chemical classes.

IntegralVac: A Machine Learning-Based Comprehensive Multivalent Epitope Vaccine Design Method

In the growing field of vaccine design for COVID and cancer research, it is essential to predict accurate peptide binding affinity and immunogenicity. We developed a comprehensive machine learning method, ‘IntegralVac,’ by integrating three existing deep learning tools: DeepVacPred, MHCSeqNet, and HemoPI. IntegralVac makes predictions for single and multivalent cancer and COVID-19 epitopes without manually selecting epitope prediction possibilities. We performed several rounds of optimization before integration, then re-trained IntegralVac for multiple datasets. We validated the IntegralVac with 4500 human cancer MHC I peptides obtained from the Immune Epitope Database (IEDB) and with cancer and COVID epitopes previously selected in our laboratory. The other data referenced from existing deep learning tools served as a positive control to ensure successful prediction was possible. As evidenced by increased accuracy and AUC, IntegralVac improved the prediction rate of top-ranked epitopes. We also examined the compatibility between other servers’ clinical checkpoint filters and IntegralVac. This was to ensure that the other servers had a means for predicting additional checkpoint filters that we wanted to implement in IntegralVac. The clinical checkpoint filters, including allergenicity, antigenicity, and toxicity, were used as additional predictors to improve IntegralVac’s prediction accuracy. We generated immunogenicity scores by cross-comparing sequence inputs with each other and determining the overlap between each individual peptide sequence. The IntegralVac increased the immunogenicity prediction accuracy to 90.1% AUC and the binding affinity accuracy to 95.4% compared to the control NetMHCPan server. The IntegralVac opens new avenues for future in silico methods, by building upon established models for continued prediction accuracy improvement.

A novel chemo-phenotypic method identifies mixtures of salpn, vitamin D3, and pesticides involved in the development of colorectal and pancreatic cancer

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_ts), which serves as a holistic metric of potential toxicity and disease outcome. CPTM quantitative toxicity is the measure of chemical features, biological phenotypic effects, and toxicokinetic properties of the ECs. For proof-of-concept, we subject ECs obtained from the Environmental Protection Agency's (EPA) database to the CPTM. We validated the CPTM toxicity predictions by correlating 'Z_ts' scores with known toxicity effects. We also confirmed the CPTM predictions with in-vitro, and in-vivo experiments. In in-vitro and zebrafish models, we showed that, mixtures of the motor oil and food additive 'Salpn' with endogenous nuclear receptor ligands such as Vitamin D3, dysregulated the nuclear receptors and key transcription pathways involved in Colorectal Cancer. Further, in a human patient derived cell organoid model, we found that a mixture of the widely used pesticides 'Tetramethrin' and 'Fenpropathrin' significantly impacts the population of patient derived pancreatic cancer cells and 3D organoid models to support rapid PDAC disease progression. The CPTM method is, to our knowledge, the first comprehensive toxico-physicochemical, and phenotypic bionetwork-based platform for efficient high-throughput screening of environmental chemical toxicity, mechanisms of action, and connection to disease outcomes.

A New Epitope Selection Method: Application to Design a Multi-Valent Epitope Vaccine Targeting HRAS Oncogene in Squamous Cell Carcinoma

We developed an epitope selection method for the design of MHC targeting peptide vaccines. The method utilizes predictions for several clinical checkpoint filters, including binding affinity, immunogenicity, antigenicity, half-life, toxicity, IFNγ release, and instability. The accuracy of the prediction tools for these filter variables was confirmed using experimental data obtained from the Immune Epitope Database (IEDB). We also developed a graphical user interface computational tool called 'PCOptim' to assess the success of an epitope filtration method. To validate the filtration methods, we used a large data set of experimentally determined, immunogenic SARS-CoV-2 epitopes, which were obtained from a meta-analysis. The validation process proved that placing filters on individual parameters was the most effective method to select top epitopes. For a proof-of-concept, we designed epitope-based vaccine candidates for squamous cell carcinoma, selected from the top mutated epitopes of the HRAS gene. By comparing the filtered epitopes to PCOptim's output, we assessed the success of the epitope selection method. The top 15 mutations in squamous cell carcinoma resulted in 16 CD8 epitopes which passed the clinical checkpoints filters. Notably, the identified HRAS epitopes are the same as the clinical immunogenic HRAS epitope-based vaccine candidates identified by the previous studies. This indicates further validation of our filtration method. We expect a similar turn-around for the other designed HRAS epitopes as a vaccine candidate for squamous cell carcinoma. Furthermore, we obtained a world population coverage of 89.45% for the top MHC Class I epitopes and 98.55% population coverage in the absence of the IFNγ release clinical checkpoint filter. We also identified some of the predicted human epitopes to be strong binders to murine MHC molecules, which provides insight into studying their immunogenicity in preclinical models. Further investigation in murine models could warrant the application of these epitopes for treatment or prevention of squamous cell carcinoma.

The E3 ubiquitin ligase component, Cereblon, is an evolutionarily conserved regulator of Wnt signaling

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 CRL4^CRBN 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.

Immune characterization and profiles of SARS-CoV-2 infected patients reveals potential host therapeutic targets and SARS-CoV-2 oncogenesis mechanism

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.

Small compounds targeting tyrosine phosphorylation of Scaffold Protein Receptor for Activated C Kinase1A (RACK1A) regulate auxin mediated lateral root development in Arabidopsis

Receptor for activated C kinase 1 (RACK1) is WD-40 type scaffold protein, conserved in all eukaryote organisms. Many reports implicated RACK1 in plant hormone signal transduction pathways including in auxin and diverse stress signaling pathways; however, the precise molecular mechanism of its role is not understood. Previously, a group of small compounds targeting the Arabidopsis RACK1A functional site-Tyr²⁴⁸ have been developed. Here, the three different small compounds are used to elucidate the role of RACK1A in auxin mediated lateral root development. Through monitoring the auxin response in the architecture of lateral roots and auxin reporter assays, a small molecule- SD29-12 was found to stabilize the auxin induced RACK1A Tyr²⁴⁸ phosphorylation, thereby stimulating auxin signaling and inducing lateral roots formation. In contrast, two other compounds, SD29 and SD29-14, inhibited auxin induced RACK1A Tyr²⁴⁸ phosphorylation resulting in the inhibition of auxin sensitivity and alternation in the lateral roots formation. Taken together, auxin induced RACK1A Tyr²⁴⁸ phosphorylation is found to be the critical regulatory mechanism for auxin-mediated lateral root development. This work leads to the molecular understanding of the role RACK1A plays in the auxin induced lateral root development signaling pathways. The auxin signal stimulating compound has the potential to be used as auxin-based root inducing bio-stimulant.

Immune characterization and profiles of SARS-CoV-2 infected patients reveals potential host therapeutic targets and SARS-CoV-2 oncogenesis mechanism

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.

Cadherin 11 Promotes Immunosuppression and Extracellular Matrix Deposition to Support Growth of Pancreatic Tumors and Resistance to Gemcitabine in Mice

BACKGROUND & AIMS

Pancreatic ductal adenocarcinomas (PDACs) 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 CDH11, which has been associated with other fibrotic disorders and is expressed by activated fibroblasts.

METHODS

We compared levels of CDH11 messenger RNA in human pancreatitis and pancreatic cancer tissues and cells 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 messenger RNA 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 of KPC/Cdh11^+/- and KPC/Cdh11^-/- mice only or reduced subcutaneous tumor growth in mT3 engrafted Cdh11^+/+ mice when 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.

Machine and deep learning approaches for cancer drug repurposing

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.

Predicting new drug indications for prostate cancer: The integration of an in silico proteochemometric network pharmacology platform with patient-derived primary prostate cells

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.

Host targeted antiviral (HTA): functional inhibitor compounds of scaffold protein RACK1 inhibit herpes simplex virus proliferation

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.

Harnessing Polypharmacology with Computer-Aided Drug Design and Systems Biology

The ascent of polypharmacology in drug development has many implications for disease therapy, most notably in the efforts of drug discovery, drug repositioning, precision medicine and combination therapy. The single- target approach to drug development has encountered difficulties in predicting drugs that are both clinically efficacious and avoid toxicity. By contrast, polypharmacology offers the possibility of a controlled distribution of effects on a biological system. This review addresses possibilities and bottlenecks in the efficient computational application of polypharmacology. The two major areas we address are the discovery and prediction of multiple protein targets using the tools of computer-aided drug design, and the use of these protein targets in predicting therapeutic potential in the context of biological networks. The successful application of polypharmacology to systems biology and pharmacology has the potential to markedly accelerate the pace of development of novel therapies for multiple diseases, and has implications for the intellectual property landscape, likely requiring targeted changes in patent law.

Differential prioritization of therapies to subtypes of triple negative breast cancer using a systems medicine method

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.

The repurposed anthelmintic mebendazole in combination with trametinib suppresses refractory NRASQ61K melanoma

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.

Prediction of Chemical Multi-target Profiles and Adverse Outcomes with Systems Toxicology

The field of systems biology, termed systems toxicology when applied to the characterization of adverse outcomes following chemical exposure, seeks to develop biological networks to explain phenotypic responses. Ideally, these are qualitatively and quantitatively similar to the actual network of biological entities that have functional consequences in living organisms. In this review, computational tools for predicting chemicalprotein interactions of multi-target compounds are outlined. Then, we discuss how the methods of systems toxicology currently draw on those interactions to predict resulting adverse outcomes which include diseases, adverse drug reactions, and toxic endpoints. These methods are useful for predicting the safety of drugs in drug development and the toxicity of environmental chemicals (ECs) in environmental toxicology.

Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools

BACKGROUND

While establishing efficacy in translational models and humans through clinically-relevant endpoints for disease is of great interest, assessing the potential toxicity of a putative therapeutic drug is critical. Toxicological assessments in the pre-clinical discovery phase help to avoid future failure in the clinical phases of drug development. Many in vitro assays exist to aid in modular toxicological assessment, such as hepatotoxicity and genotoxicity. While these methods have provided tremendous insight into human toxicity by investigational new drugs, they are expensive, require substantial resources, and do not account for pharmacogenomics as well as critical ADME properties. Computational tools can fill this niche in toxicology if in silico models are accurate in relating drug molecular properties to toxicological endpoints as well as reliable in predicting important drug-target interactions that mediate known adverse events or adverse outcome pathways (AOPs).

METHODS

We undertook an unstructured search of multiple bibliographic databases for peer-reviewed literature regarding computational methods in predictive toxicology for in silico drug discovery. As this review paper is meant to serve as a survey of available methods for the interested reader, no focused criteria were applied. Literature chosen was based on the writers' expertise and intent in communicating important aspects of in silico toxicology to the interested reader.

CONCLUSION

This review provides a purview of computational methods of pre-clinical toxicologic assessments for novel small molecule drugs that may be of use for novice and experienced investigators as well as academic and commercial drug discovery entities.

MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links

BACKGROUND

Cancer-associated metabolites result from cell-wide mechanisms of dysregulation. The field of metabolomics has sought to identify these aberrant metabolites as disease biomarkers, clues to understanding disease mechanisms, or even as therapeutic agents.

OBJECTIVE

This study was undertaken to reliably predict metabolites associated with colorectal, esophageal, and prostate cancers. Metabolite and disease biological action networks were compared in a computational platform called MSD-MAP (Multi Scale Disease-Metabolite Association Platform).

METHODS

Using differential gene expression analysis with patient-based RNAseq data from The Cancer Genome Atlas, genes up- or down-regulated in cancer compared to normal tissue were identified. Relational databases were used to map biological entities including pathways, functions, and interacting proteins, to those differential disease genes. Similar relational maps were built for metabolites, stemming from known and in silico predicted metabolite-protein associations. The hypergeometric test was used to find statistically significant relationships between disease and metabolite biological signatures at each tier, and metabolites were assessed for multi-scale association with each cancer. Metabolite networks were also directly associated with various other diseases using a disease functional perturbation database.

RESULTS

Our platform recapitulated metabolite-disease links that have been empirically verified in the scientific literature, with network-based mapping of jointly-associated biological activity also matching known disease mechanisms. This was true for colorectal, esophageal, and prostate cancers, using metabolite action networks stemming from both predicted and known functional protein associations.

CONCLUSION

By employing systems biology concepts, MSD-MAP reliably predicted known cancermetabolite links, and may serve as a predictive tool to streamline conventional metabolomic profiling methodologies.

Abstract 3860: The repurposed anthelmintic mebendazole in combination with trametinib suppresses refractory NRASQ61K melanoma

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 MBZ binds and inhibits kinases important in cancer, especially both BRAFWT and BRAFV600E. We find that MBZ synergizes with the MEK inhibitor trametinib to inhibit growth of dabrafenib-resistant BRAFWT-NRASQ61K melanoma cells in culture and in xenografts, and markedly decreased MEK and ERK phosphorylation in vivo. 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 downregulated, 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, an off-patent approved drug with no major side effects, should be considered as a therapeutic option in combination with trametinib, for patients with NRASQ61mut or other non-V600E BRAF mutant melanomas.

Citation Format: Cynthia M. Simbulan-Rosenthal, Sivanesan Dakshanamurthy, Anirudh Gaur, You-Shin Chen, Rena Shimizu, Maryam AbdusSumad, Hengbo Zhou, John Zapas, Valerie Calvert, Emaneul F. Petricoin, Michael B. Atkins, Stephen W. Byers, Dean S. Rosenthal. The repurposed anthelmintic mebendazole in combination with trametinib suppresses refractory NRASQ61K melanoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3860.

RepurposeVS: A Drug Repurposing-Focused Computational Method for Accurate Drug-Target Signature Predictions

We describe here RepurposeVS for the reliable prediction of drug-target signatures using X-ray protein crystal structures. RepurposeVS is a virtual screening method that incorporates docking, drug-centric and protein-centric 2D/3D fingerprints with a rigorous mathematical normalization procedure to account for the variability in units and provide high-resolution contextual information for drug-target binding. Validity was confirmed by the following: (1) providing the greatest enrichment of known drug binders for multiple protein targets in virtual screening experiments, (2) determining that similarly shaped protein target pockets are predicted to bind drugs of similar 3D shapes when RepurposeVS is applied to 2,335 human protein targets, and (3) determining true biological associations in vitro for mebendazole (MBZ) across many predicted kinase targets for potential cancer repurposing. Since RepurposeVS is a drug repurposing-focused method, benchmarking was conducted on a set of 3,671 FDA approved and experimental drugs rather than the Database of Useful Decoys (DUDE) so as to streamline downstream repurposing experiments. We further apply RepurposeVS to explore the overall potential drug repurposing space for currently approved drugs. RepurposeVS is not computationally intensive and increases performance accuracy, thus serving as an efficient and powerful in silico tool to predict drug-target associations in drug repurposing.

DrugGenEx-Net: a novel computational platform for systems pharmacology and gene expression-based drug repurposing

Background

The targeting of disease-related proteins is important for drug discovery, and yet target-based discovery has not been fruitful. Contextualizing overall biological processes is critical to formulating successful drug-disease hypotheses. Network pharmacology helps to overcome target-based bottlenecks through systems biology analytics, such as protein-protein interaction (PPI) networks and pathway regulation.

Results

We present a systems polypharmacology platform entitled DrugGenEx-Net (DGE-NET). DGE-NET predicts empirical drug-target (DT) interactions, integrates interaction pairs into a multi-tiered network analysis, and ultimately predicts disease-specific drug polypharmacology through systems-based gene expression analysis. Incorporation of established biological network annotations for protein target-disease, −signaling pathway, −molecular function, and protein-protein interactions enhances predicted DT effects on disease pathophysiology. Over 50 drug-disease and 100 drug-pathway predictions are validated. For example, the predicted systems pharmacology of the cholesterol-lowering agent ezetimibe corroborates its potential carcinogenicity.

When disease-specific gene expression analysis is integrated, DGE-NET prioritizes known therapeutics/experimental drugs as well as their contra-indications. Proof-of-concept is established for immune-related rheumatoid arthritis and inflammatory bowel disease, as well as neuro-degenerative Alzheimer’s and Parkinson’s diseases.

Conclusions

DGE-NET is a novel computational method that predicting drug therapeutic and counter-therapeutic indications by uniquely integrating systems pharmacology with gene expression analysis. DGE-NET correctly predicts various drug-disease indications by linking the biological activity of drugs and diseases at multiple tiers of biological action, and is therefore a useful approach to identifying drug candidates for re-purposing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1065-y) contains supplementary material, which is available to authorized users.

Cadherin-11 in poor prognosis malignancies and rheumatoid arthritis: common target, common therapies

Cadherin-11 (CDH11), associated with epithelial to mesenchymal transformation in development, poor prognosis malignancies and cancer stem cells, is also a major therapeutic target in rheumatoid arthritis (RA). CDH11 expressing basal-like breast carcinomas and other CDH11 expressing malignancies exhibit poor prognosis. We show that CDH11 is increased early in breast cancer and ductal carcinoma in-situ. CDH11 knockdown and antibodies effective in RA slowed the growth of basal-like breast tumors and decreased proliferation and colony formation of breast, glioblastoma and prostate cancer cells. The repurposed arthritis drug celecoxib, which binds to CDH11, and other small molecules designed to bind CDH11 without inhibiting COX-2 preferentially affect the growth of CDH11 positive cancer cells in vitro and in animals. These data suggest that CDH11 is important for malignant progression, and is a therapeutic target in arthritis and cancer with the potential for rapid clinical translation

Naturally occurring polyphenol, morin hydrate, inhibits enzymatic activity of N-methylpurine DNA glycosylase, a DNA repair enzyme with various roles in human disease

Interest in the mechanisms of DNA repair pathways, including the base excision repair (BER) pathway specifically, has heightened since these pathways have been shown to modulate important aspects of human disease. Modulation of the expression or activity of a particular BER enzyme, N-methylpurine DNA glycosylase (MPG), has been demonstrated to play a role in carcinogenesis and resistance to chemotherapy as well as neurodegenerative diseases, which has intensified the focus on studying MPG-related mechanisms of repair. A specific small molecule inhibitor for MPG activity would be a valuable biochemical tool for understanding these repair mechanisms. By screening several small molecule chemical libraries, we identified a natural polyphenolic compound, morin hydrate, which inhibits MPG activity specifically (IC50=2.6μM). Detailed mechanism analysis showed that morin hydrate inhibited substrate DNA binding of MPG, and eventually the enzymatic activity of MPG. Computational docking studies with an x-ray derived MPG structure as well as comparison studies with other structurally-related flavonoids offer a rationale for the inhibitory activity of morin hydrate observed. The results of this study suggest that the morin hydrate could be an effective tool for studying MPG function and it is possible that morin hydrate and its derivatives could be utilized in future studies focused on the role of MPG in human disease.

Germ line variants of human N-methylpurine DNA glycosylase show impaired DNA repair activity and facilitate 1,N6-ethenoadenine-induced mutations

Human N-methylpurine DNA glycosylase (hMPG) initiates base excision repair of a number of structurally diverse purine bases including 1,N(6)-ethenoadenine, hypoxanthine, and alkylation adducts in DNA. Genetic studies discovered at least eight validated non-synonymous single nucleotide polymorphisms (nsSNPs) of the hMPG gene in human populations that result in specific single amino acid substitutions. In this study, we tested the functional consequences of these nsSNPs of hMPG. Our results showed that two specific arginine residues, Arg-141 and Arg-120, are important for the activity of hMPG as the germ line variants R120C and R141Q had reduced enzymatic activity in vitro as well as in mammalian cells. Expression of these two variants in mammalian cells lacking endogenous MPG also showed an increase in mutations and sensitivity to an alkylating agent compared with the WT hMPG. Real time binding experiments by surface plasmon resonance spectroscopy suggested that these variants have substantial reduction in the equilibrium dissociation constant of binding (KD) of hMPG toward 1,N(6)-ethenoadenine-containing oligonucleotide (ϵA-DNA). Pre-steady-state kinetic studies showed that the substitutions at arginine residues affected the turnover of the enzyme significantly under multiple turnover condition. Surface plasmon resonance spectroscopy further showed that both variants had significantly decreased nonspecific (undamaged) DNA binding. Molecular modeling suggested that R141Q substitution may have resulted in a direct loss of the salt bridge between ϵA-DNA and hMPG, whereas R120C substitution redistributed, at a distance, the interactions among residues in the catalytic pocket. Together our results suggest that individuals carrying R120C and R141Q MPG variants may be at risk for genomic instability and associated diseases as a consequence.

A specific missense mutation in GTF2I occurs at high frequency in thymic epithelial tumors

We analyzed 28 thymic epithelial tumors (TETs) using next-generation sequencing and identified a missense mutation (chromosome 7 c.74146970T>A) in GTF2I at high frequency in type A thymomas, a relatively indolent subtype. In a series of 274 TETs, we detected the GTF2I mutation in 82% of type A and 74% of type AB thymomas but rarely in the aggressive subtypes, where recurrent mutations of known cancer genes have been identified. Therefore, GTF2I mutation correlated with better survival. GTF2I β and δ isoforms were expressed in TETs, and both mutant isoforms were able to stimulate cell proliferation in vitro. Thymic carcinomas carried a higher number of mutations than thymomas (average of 43.5 and 18.4, respectively). Notably, we identified recurrent mutations of known cancer genes, including TP53, CYLD, CDKN2A, BAP1 and PBRM1, in thymic carcinomas. These findings will complement the diagnostic assessment of these tumors and also facilitate development of a molecular classification and assessment of prognosis and treatment strategies.

Drug repurposing: translational pharmacology, chemistry, computers and the clinic

The process of discovering a pharmacological compound that elicits a desired clinical effect with minimal side effects is a challenge. Prior to the advent of high-performance computing and large-scale screening technologies, drug discovery was largely a serendipitous endeavor, as in the case of thalidomide for erythema nodosum leprosum or cancer drugs in general derived from flora located in far-reaching geographic locations. More recently, de novo drug discovery has become a more rationalized process where drug-target-effect hypotheses are formulated on the basis of already known compounds/protein targets and their structures. Although this approach is hypothesis-driven, the actual success has been very low, contributing to the soaring costs of research and development as well as the diminished pharmaceutical pipeline in the United States. In this review, we discuss the evolution in computational pharmacology as the next generation of successful drug discovery and implementation in the clinic where high-performance computing (HPC) is used to generate and validate drug-target-effect hypotheses completely in silico. The use of HPC would decrease development time and errors while increasing productivity prior to in vitro, animal and human testing. We highlight approaches in chemoinformatics, bioinformatics as well as network biopharmacology to illustrate potential avenues from which to design clinically efficacious drugs. We further discuss the implications of combining these approaches into an integrative methodology for high-accuracy computational predictions within the context of drug repositioning for the efficient streamlining of currently approved drugs back into clinical trials for possible new indications.

Accelerating Virtual High-Throughput Ligand Docking: current technology and case study on a petascale supercomputer

In this paper we give the current state of high-throughput virtual screening. We describe a case study of using a task-parallel MPI (Message Passing Interface) version of Autodock4 [1], [2] to run a virtual high-throughput screen of one-million compounds on the Jaguar Cray XK6 Supercomputer at Oak Ridge National Laboratory. We include a description of scripts developed to increase the efficiency of the predocking file preparation and postdocking analysis. A detailed tutorial, scripts, and source code for this MPI version of Autodock4 are available online at http://www.bio.utk.edu/baudrylab/autodockmpi.htm.

Big data: the next frontier for innovation in therapeutics and healthcare

Advancements in genomics and personalized medicine not only effect healthcare delivery from patient and provider standpoints, but also reshape biomedical discovery. We are in the era of the '-omics', wherein an individual's genome, transcriptome, proteome and metabolome can be scrutinized to the finest resolution to paint a personalized biochemical fingerprint that enables tailored treatments, prognoses, risk factors, etc. Digitization of this information parlays into 'big data' informatics-driven evidence-based medical practice. While individualized patient management is a key beneficiary of next-generation medical informatics, this data also harbors a wealth of novel therapeutic discoveries waiting to be uncovered. 'Big data' informatics allows for networks-driven systems pharmacodynamics whereby drug information can be coupled to cellular- and organ-level physiology for determining whole-body outcomes. Patient '-omics' data can be integrated for ontology-based data-mining for the discovery of new biological associations and drug targets. Here we highlight the potential of 'big data' informatics for clinical pharmacology.

Allelic variation of killer cell immunoglobulin-like receptor 2DS5 impacts glycosylation altering cell surface expression levels

Natural killer cell stimulatory receptor gene, KIR2DS5, is polymorphic. While KIR2DS5*002 is most frequently observed, other alleles have also been found. The proteins encoded by these alleles (KIR2DS5*002-*009) are expressed at varying levels on the surface of NKL and Jurkat transfectants. Gel electrophoresis of all allelic products showed two isoforms which differ in the extent of maturation of N-linked glycosylation. These isoforms differed in intensity and molecular weight among the allelic products. Site-directed mutagenesis was used to identify polymorphic variation at residues 123 and 157 as key in altering glycosylation and levels of surface expression.

Design, synthesis and discovery of picomolar selective α4β2 nicotinic acetylcholine receptor ligands

Developing novel and selective compounds that desensitize α4β2 nicotinic acetylcholine receptors (nAChRs) could provide new effective treatments for nicotine addiction, as well as other disorders. Here we report a new class of nAChR ligands that display high selectivity and picomolar binding affinity for α4β2 nicotinic receptors. The novel compounds have Ki values in the range of 0.031-0.26 nM and properties that should make them good candidates as drugs acting in the CNS. The selected lead compound 1 (VMY-2-95) binds with high affinity and potently desensitizes α4β2 nAChRs. At a dose of 3 mg/kg, compound 1 significantly reduced rat nicotine self-administration. The overall results support further characterizations of compound 1 and its analogues in preclinical models of nicotine addiction and perhaps other disorders involving nAChRs.

Abstract A045: Cadherin-11, a common therapeutic target in poor prognosis malignancies and rheumatoid arthritis

Rational: Poor prognosis epithelial-derived cancers often exhibit morphologic and molecular changes characteristic of an epithelial to mesenchymal transition (EMT). EMT markers are predominantly found in tumors with a basal-like phenotype. Increased expression of the mesenchymal cadherins N-cadherin and/or Cadherin-11 (CDH11) and decreased E-cadherin, have been associated with both EMT and tumor progression. Importantly, CDH11 is a therapeutic target in rheumatoid arthritis (RA), an inflammatory disease with properties often compared with cancer. As CDH11 antibody based therapeutics are in clinical trials for RA and we recently showed that the arthritis drug celecoxib has the structural potential to bind CDH11, there is a strong possibility that, if CDH11can be shown to drive malignant progression rather than simply be associated with it, therapeutic options may be rapidly developed.

Results: We show that CDH11 is increased early in breast cancer and ductal carcinoma in-situ. CDH11 knockdown and antibodies effective in RA slowed the growth of basal-like breast tumors. CDH11 attenuation decreased proliferation and colony formation of breast, glioblastoma and prostate cancer cells. The arthritis drug celecoxib, which has characteristics consistent with CDH11 binding, an analogue without COX-2 inhibitory activity and other molecules that target CDH11, preferentially affect CDH11 positive cancer cells.

Conclusion: Our work demonstrates that cadherin-11 is important for malignant progression, is a therapeutic target in several aggressive tumors and introduces an antibody, novel small molecules and an approved arthritis drug that inhibit its function with potential for rapid clinical translation.

Citation Format: Shahin Assefnia, Sivanesan Dakshanamurthy, Jaime M. Guidry Auvil, Constanze Hampel, Panos Anastasiadis, Bhaskar Kallakury, Aykut Uren, David W. Foley, Milton Brown, Lawrence Shapiro, Michael Brenner, David Haigh, Stephen W. Byers. Cadherin-11, a common therapeutic target in poor prognosis malignancies and rheumatoid arthritis. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications; Oct 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2013;11(10 Suppl):Abstract nr A045.

Allelic variation in KIR2DL3 generates a KIR2DL2-like receptor with increased binding to its HLA-C ligand

Although extensive homology exists between their extracellular domains, NK cell inhibitory receptors killer Ig-like receptor (KIR) 2DL2*001 and KIR2DL3*001 have previously been shown to differ substantially in their HLA-C binding avidity. To explore the largely uncharacterized impact of allelic diversity, the most common KIR2DL2/3 allelic products in European American and African American populations were evaluated for surface expression and binding affinity to their HLA-C group 1 and 2 ligands. Although no significant differences in the degree of cell membrane localization were detected in a transfected human NKL cell line by flow cytometry, surface plasmon resonance and KIR binding to a panel of HLA allotypes demonstrated that KIR2DL3*005 differed significantly from other KIR2DL3 allelic products in its ability to bind HLA-C. The increased affinity and avidity of KIR2DL3*005 for its ligand was also demonstrated to have a larger impact on the inhibition of IFN-γ production by the human KHYG-1 NK cell line compared with KIR2DL3*001, a low-affinity allelic product. Site-directed mutagenesis established that the combination of arginine at residue 11 and glutamic acid at residue 35 in KIR2DL3*005 were critical to the observed phenotype. Although these residues are distal to the KIR/HLA-C interface, molecular modeling suggests that alteration in the interdomain hinge angle of KIR2DL3*005 toward that found in KIR2DL2*001, another strong receptor of the KIR2DL2/3 family, may be the cause of this increased affinity. The regain of inhibitory capacity by KIR2DL3*005 suggests that the rapidly evolving KIR locus may be responding to relatively recent selective pressures placed upon certain human populations.

Predicting new indications for approved drugs using a proteochemometric method

The most effective way to move from target identification to the clinic is to identify already approved drugs with the potential for activating or inhibiting unintended targets (repurposing or repositioning). This is usually achieved by high throughput chemical screening, transcriptome matching, or simple in silico ligand docking. We now describe a novel rapid computational proteochemometric method called "train, match, fit, streamline" (TMFS) to map new drug-target interaction space and predict new uses. The TMFS method combines shape, topology, and chemical signatures, including docking score and functional contact points of the ligand, to predict potential drug-target interactions with remarkable accuracy. Using the TMFS method, we performed extensive molecular fit computations on 3671 FDA approved drugs across 2335 human protein crystal structures. The TMFS method predicts drug-target associations with 91% accuracy for the majority of drugs. Over 58% of the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%, 84%, and 91% for agents ranked in the top 10, 20, 30, and 40, respectively, out of all 3671 drugs. Drugs ranked in the top 1-40 that have not been experimentally validated for a particular target now become candidates for repositioning. Furthermore, we used the TMFS method to discover that mebendazole, an antiparasitic with recently discovered and unexpected anticancer properties, has the structural potential to inhibit VEGFR2. We confirmed experimentally that mebendazole inhibits VEGFR2 kinase activity and angiogenesis at doses comparable with its known effects on hookworm. TMFS also predicted, and was confirmed with surface plasmon resonance, that dimethyl celecoxib and the anti-inflammatory agent celecoxib can bind cadherin-11, an adhesion molecule important in rheumatoid arthritis and poor prognosis malignancies for which no targeted therapies exist. We anticipate that expanding our TMFS method to the >27 000 clinically active agents available worldwide across all targets will be most useful in the repositioning of existing drugs for new therapeutic targets.

Synthesis and biological evaluation of a fluorescent analog of phenytoin as a potential inhibitor of neuropathic pain and imaging agent

Here we report on a novel fluorescent analog of phenytoin as a potential inhibitor of neuropathic pain with potential use as an imaging agent. Compound 2 incorporated a heptyl side chain and dansyl moiety onto the parent compound phenytoin and produced greater displacement of BTX from sodium channels and greater functional blockade with greatly reduced toxicity. Compound 2 reduced mechano-allodynia in a rat model of neuropathic pain and was visualized ex vivo in sensory neuron axons with two-photon microscopy. These results suggest a promising strategy for developing novel sodium channel inhibitors with imaging capabilities.

Tyrosine-phosphorylated caveolin-1 (Tyr-14) increases sensitivity to paclitaxel by inhibiting BCL2 and BCLxL proteins via c-Jun N-terminal kinase (JNK)

Paclitaxel, an anti-microtubule agent, is an effective chemotherapeutic drug in breast cancer. Nonetheless, resistance to paclitaxel remains a major clinical challenge. The need to better understand the resistant phenotype and to find biomarkers that could predict tumor response to paclitaxel is evident. In estrogen receptor α-positive (ER(+)) breast cancer cells, phosphorylation of caveolin-1 (CAV1) on Tyr-14 facilitates mitochondrial apoptosis by increasing BCL2 phosphorylation in response to low dose paclitaxel (10 nM). However, two variants of CAV1 exist: the full-length form, CAV1α (wild-type CAV1 or wtCAV1), and a truncated form, CAV1β. Only wtCAV1 has the Tyr-14 region at the N terminus. The precise cellular functions of CAV1 variants are unknown. We now show that CAV1 variants play distinct roles in paclitaxel-mediated cell death/survival. CAV1β expression is increased in paclitaxel-resistant cells when compared with sensitive cells. Expression of CAV1β in sensitive cells significantly reduces their responsiveness to paclitaxel. These activities reflect an essential role for Tyr-14 phosphorylation because wtCAV1 expression, but not a phosphorylation-deficient mutant (Y14F), inactivates BCL2 and BCLxL through activation of c-Jun N-terminal kinase (JNK). MCF-7 cells that express Y14F are resistant to paclitaxel and are resensitized by co-treatment with ABT-737, a BH3-mimetic small molecule inhibitor. Using structural homology modeling, we propose that phosphorylation on Tyr-14 enables a favorable conformation for proteins to bind to the CAV1 scaffolding domain. Thus, we highlight novel roles for CAV1 variants in cell death; wtCAV1 promotes cell death, whereas CAV1β promotes cell survival by preventing inactivation of BCL2 and BCLxL via JNK in paclitaxel-mediated apoptosis.

Mechanisms of unphosphorylated STAT3 transcription factor binding to DNA

Phosphorylation of signal transducer and activator of transcription 3 (STAT3) on a single tyrosine residue in response to growth factors, cytokines, interferons, and oncogenes activates its dimerization, translocation to the nucleus, binding to the interferon γ (gamma)-activated sequence (GAS) DNA-binding site and activation of transcription of target genes. STAT3 is constitutively phosphorylated in various cancers and drives gene expression from GAS-containing promoters to promote tumorigenesis. Recently, roles for unphosphorylated STAT3 (U-STAT3) have been described in response to cytokine stimulation, in cancers, and in maintenance of heterochromatin stability. However, the mechanisms underlying U-STAT3 binding to DNA has not been fully investigated. Here, we explore STAT3-DNA interactions by atomic force microscopy (AFM) imaging. We observed that U-STAT3 molecules bind to the GAS DNA-binding site as dimers and monomers. In addition, we observed that U-STAT3 binds to AT-rich DNA sequence sites and recognizes specific DNA structures, such as 4-way junctions and DNA nodes, within negatively supercoiled plasmid DNA. These structures are important for chromatin organization and our data suggest a role for U-STAT3 as a chromatin/genome organizer. Unexpectedly, we found that a C-terminal truncated 67.5-kDa STAT3 isoform recognizes single-stranded spacers within cruciform structures that also have a role in chromatin organization and gene expression. This isoform appears to be abundant in the nuclei of cancer cells and, therefore, may have a role in regulation of gene expression. Taken together, our data highlight novel mechanisms by which U-STAT3 binds to DNA and supports U-STAT3 function as a transcriptional activator and a chromatin/genomic organizer.

Polygamain, a new microtubule depolymerizing agent that occupies a unique pharmacophore in the colchicine site

Bioassay-guided fractionation was used to isolate the lignan polygamain as the microtubule-active constituent in the crude extract of the Mountain torchwood, Amyris madrensis. Similar to the effects of the crude plant extract, polygamain caused dose-dependent loss of cellular microtubules and the formation of aberrant mitotic spindles that led to G(2)/M arrest. Polygamain has potent antiproliferative activities against a wide range of cancer cell lines, with an average IC(50) of 52.7 nM. Clonogenic studies indicate that polygamain effectively inhibits PC-3 colony formation and has excellent cellular persistence after washout. In addition, polygamain is able to circumvent two clinically relevant mechanisms of drug resistance, the expression of P-glycoprotein and the βIII isotype of tubulin. Studies with purified tubulin show that polygamain inhibits the rate and extent of purified tubulin assembly and displaces colchicine, indicating a direct interaction of polygamain within the colchicine binding site on tubulin. Polygamain has structural similarities to podophyllotoxin, and molecular modeling simulations were conducted to identify the potential orientations of these compounds within the colchicine binding site. These studies suggest that the benzodioxole group of polygamain occupies space similar to the trimethoxyphenyl group of podophyllotoxin but with distinct interactions within the hydrophobic pocket. Our results identify polygamain as a new microtubule destabilizer that seems to occupy a unique pharmacophore within the colchicine site of tubulin. This new pharmacophore will be used to design new colchicine site compounds that might provide advantages over the current agents.

Asymmetric synthesis and evaluation of a hydroxyphenylamide voltage-gated sodium channel blocker in human prostate cancer xenografts

Voltage-gated sodium channels are known to be expressed in neurons and other excitable cells. Recently, voltage-gated sodium channels have been found to be expressed in human prostate cancer cells. α-Hydroxy-α-phenylamides are a new class of small molecules that have demonstrated potent inhibition of voltage-gated sodium channels. The hydroxyamide motif, an isostere of a hydantoin ring, provides an active scaffold from which several potent racemic sodium channel blockers have been derived. With little known about chiral preferences, the development of chiral syntheses to obtain each pure enantiomer for evaluation as sodium channel blockers is important. Using Seebach and Frater's chiral template, cyclocondensation of (R)-3-chloromandelic acid with pivaldehyde furnished both the cis- and trans-2,5-disubsituted dioxolanones. Using this chiral template, we synthesized both enantiomers of 2-(3-chlorophenyl)-2-hydroxynonanamide, and evaluated their ability to functionally inhibit hNa(v) isoforms, human prostate cancer cells and xenograft. Enantiomers of lead demonstrated significant ability to reduce prostate cancer in vivo.

Histone deacetylase cytoplasmic trapping by a novel fluorescent HDAC inhibitor

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.