Relating Chemical Structure to Cellular Response: An Integrative Analysis of Gene Expression, Bioactivity, and Structural Data Across 11,000 Compounds

BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity Representation

Drug failure during experimental procedures due to low bioactivity presents a significant challenge. To mitigate this risk and enhance compound bioactivities, predicting bioactivity classes during lead optimization is essential. The existing studies on structure-activity relationships have highlighted the connection between the chemical structures of compounds and their bioactivity. However, these studies often overlook the intricate relationship between drugs and bioactivity, which encompasses multiple factors beyond the chemical structure alone. To address this issue, we propose the BioAct-Het model, employing a heterogeneous siamese neural network to model the complex relationship between drugs and bioactivity classes, bringing them into a unified latent space. In particular, we introduce a novel representation for the bioactivity classes, called Bio-Prof, and enhance the original bioactivity data sets to tackle data scarcity. These innovative approaches resulted in our model outperforming the previous ones. The evaluation of BioAct-Het is conducted through three distinct strategies: association-based, bioactivity class-based, and compound-based. The association-based strategy utilizes supervised learning classification, while the bioactivity class-based strategy adopts a retrospective study evaluation approach. On the other hand, the compound-based strategy demonstrates similarities to the concept of meta-learning. Furthermore, the model's effectiveness in addressing real-world problems is analyzed through a case study on the application of vancomycin and oseltamivir for COVID-19 treatment as well as molnupiravir's potential efficacy in treating COVID-19 patients. The data and code underlying this article are available on https://github.com/CBRC-lab/BioAct-Het. However, data sets were derived from sources in the public domain.

Biomod2 modeling for predicting the potential ecological distribution of three Fritillaria species under climate change

The Fritillaria species ranked as a well-known traditional medicine in China and has become rare due to excessive harvesting. To find reasonable strategy for conservation and cultivation, identification of new ecological distribution of Fritillaria species together with prediction of those responses to climate change are necessary. In terms of current occurrence records and bioclimatic variables, the suitable habitats for Fritillaria delavayi, Fritillaria taipaiensis, and Fritillaria wabuensis were predicted. In comparison with Maxent and GARP, Biomod2 obtained the best AUC, KAPPA and TSS values of larger than 0.926 and was chosen to construct model. Temperature seasonality was indicated to put the greatest influence on Fritillaria taipaiensis and Fritillaria wabuensis, while isothermality was of most importance for Fritillaria delavayi. The current suitable areas for three Fritillaria species were distributed in south-west China, accounting for approximately 17.72%, 23.06% and 20.60% of China's total area, respectively. During 2021-2100 period, the suitable habitats of F. delavayi and F. wabuensis reached the maximum under SSP585 scenario, while that of F. taipaiensis reached the maximum under SSP126 scenario. The high niche overlap among three Fritillaria species showed correlation with the chemical composition (P ≤ 0.05), while no correlation was observed between niche overlap and DNA barcodes, indicating that spatial distribution had a major influence on chemical composition in the Fritillaria species. Finally, the acquisition of species-specific habitats would contribute to decrease in habitat competition, and future conservation and cultivation of Fritillaria species.

In silico prediction and biological assessment of novel angiogenesis modulators from traditional Chinese medicine

Uncontrolled angiogenesis is a common denominator underlying many deadly and debilitating diseases such as myocardial infarction, chronic wounds, cancer, and age-related macular degeneration. As the current range of FDA-approved angiogenesis-based medicines are far from meeting clinical demands, the vast reserve of natural products from traditional Chinese medicine (TCM) offers an alternative source for developing pro-angiogenic or anti-angiogenic modulators. Here, we investigated 100 traditional Chinese medicine-derived individual metabolites which had reported gene expression in MCF7 cell lines in the Gene Expression Omnibus (GSE85871). We extracted literature angiogenic activities for 51 individual metabolites, and subsequently analysed their predicted targets and differentially expressed genes to understand their mechanisms of action. The angiogenesis phenotype was used to generate decision trees for rationalising the poly-pharmacology of known angiogenesis modulators such as ferulic acid and curculigoside and validated by an in vitro endothelial tube formation assay and a zebrafish model of angiogenesis. Moreover, using an in silico model we prospectively examined the angiogenesis-modulating activities of the remaining 49 individual metabolites. In vitro, tetrahydropalmatine and 1 beta-hydroxyalantolactone stimulated, while cinobufotalin and isoalantolactone inhibited endothelial tube formation. In vivo, ginsenosides Rb3 and Rc, 1 beta-hydroxyalantolactone and surprisingly cinobufotalin, restored angiogenesis against PTK787-induced impairment in zebrafish. In the absence of PTK787, deoxycholic acid and ursodeoxycholic acid did not affect angiogenesis. Despite some limitations, these results suggest further refinements of in silico prediction combined with biological assessment will be a valuable platform for accelerating the research and development of natural products from traditional Chinese medicine and understanding their mechanisms of action, and also for other traditional medicines for the prevention and treatment of angiogenic diseases.

Using chemical and biological data to predict drug toxicity

Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.

Knowledge Mapping of Drug Repositioning's Theme and Development

Background

In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends.

Methodology

The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field.

Results

The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning.

Conclusion

The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.

From drug repositioning to target repositioning: prediction of therapeutic targets using genetically perturbed transcriptomic signatures

Motivation

A critical element of drug development is the identification of therapeutic targets for diseases. However, the depletion of therapeutic targets is a serious problem.

Results

In this study, we propose the novel concept of target repositioning, an extension of the concept of drug repositioning, to predict new therapeutic targets for various diseases. Predictions were performed by a trans-disease analysis which integrated genetically perturbed transcriptomic signatures (knockdown of 4345 genes and overexpression of 3114 genes) and disease-specific gene transcriptomic signatures of 79 diseases. The trans-disease method, which takes into account similarities among diseases, enabled us to distinguish the inhibitory from activatory targets and to predict the therapeutic targetability of not only proteins with known target–disease associations but also orphan proteins without known associations. Our proposed method is expected to be useful for understanding the commonality of mechanisms among diseases and for therapeutic target identification in drug discovery.

Availability and implementation

Supplemental information and software are available at the following website [http://labo.bio.kyutech.ac.jp/~yamani/target_repositioning/].

Supplementary information

Supplementary data are available at Bioinformatics online.

Recent Advances in Cancer Drug Discovery Through the Use of Phenotypic Reporter Systems, Connectivity Mapping, and Pooled CRISPR Screening

Multi-omic approaches offer an unprecedented overview of the development, plasticity, and resistance of cancer. However, the translation from anti-cancer compounds identified in vitro to clinically active drugs have a notoriously low success rate. Here, we review how technical advances in cell culture, robotics, computational biology, and development of reporter systems have transformed drug discovery, enabling screening approaches tailored to clinically relevant functional readouts (e.g., bypassing drug resistance). Illustrating with selected examples of “success stories,” we describe the process of phenotype-based high-throughput drug screening to target malignant cells or the immune system. Second, we describe computational approaches that link transcriptomic profiling of cancers with existing pharmaceutical compounds to accelerate drug repurposing. Finally, we review how CRISPR-based screening can be applied for the discovery of mechanisms of drug resistance and sensitization. Overall, we explore how the complementary strengths of each of these approaches allow them to transform the paradigm of pre-clinical drug development.

An integrated network representation of multiple cancer-specific data for graph-based machine learning

Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. Emphasizing on the system-level complexity of cancer, we devised a procedure to integrate multiple heterogeneous data, including biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. In order to construct compact, yet information-rich cancer-specific networks, we developed a novel graph reduction algorithm. Driven by not only the topological information, but also the biological knowledge, the graph reduction increases the feature-only entropy while preserving the valuable graph-feature information. Subsequent comparative benchmarking simulations employing a tissue level cross-validation protocol demonstrate that the accuracy of a graph-based predictor of the drug efficacy is 0.68, which is notably higher than those measured for more traditional, matrix-based techniques on the same data. Overall, the non-Euclidean representation of the cancer-specific data improves the performance of machine learning to predict the response of cancer to pharmacotherapy. The generated data are freely available to the academic community at https://osf.io/dzx7b/.

Ibrutinib Blocks YAP1 Activation and Reverses BRAF Inhibitor Resistance in Melanoma Cells

Most B-Raf proto-oncogene (BRAF)-mutant melanoma tumors respond initially to BRAF inhibitor (BRAFi)/mitogen-activated protein kinase kinase 1 inhibitor (MEKi) therapy, although few patients have durable long-term responses to these agents. The goal of this study was to use an unbiased computational approach to identify inhibitors that reverse an experimentally derived BRAFi resistance gene expression signature. Using this approach, we found that ibrutinib effectively reverses this signature, and we demonstrate experimentally that ibrutinib resensitizes a subset of BRAFi-resistant melanoma cells to vemurafenib. Ibrutinib is used clinically as an inhibitor of the Src family kinase Bruton tyrosine kinase (BTK); however, neither BTK deletion nor treatment with acalabrutinib, another BTK inhibitor with reduced off-target activity, resensitized cells to vemurafenib. These data suggest that ibrutinib acts through a BTK-independent mechanism in vemurafenib resensitization. To better understand this mechanism, we analyzed the transcriptional profile of ibrutinib-treated BRAFi-resistant melanoma cells and found that the transcriptional profile of ibrutinib was highly similar to that of multiple Src proto-oncogene kinase inhibitors. Since ibrutinib, but not acalabrutinib, has appreciable off-target activity against multiple Src family kinases, it suggests that ibrutinib may be acting through this mechanism. Furthermore, genes that are differentially expressed in ibrutinib-treated cells are enriched in Yes1-associated transcriptional regulator (YAP1) target genes, and we showed that ibrutinib, but not acalabrutinib, reduces YAP1 activity in BRAFi-resistant melanoma cells. Taken together, these data suggest that ibrutinib, or other Src family kinase inhibitors, may be useful for treating some BRAFi/MEKi-refractory melanoma tumors. SIGNIFICANCE STATEMENT: MAPK-targeted therapies provide dramatic initial responses, but resistance develops rapidly; a subset of these tumors may be rendered sensitive again by treatment with an approved Src family kinase inhibitor-ibrutinub-potentially providing improved clinical outcomes.

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug target identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation protocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drugtarget interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through offtarget binding, and repositioning opportunities.

An information-theoretic approach for measuring the distance of organ tissue samples using their transcriptomic signatures

Motivation

Recapitulating aspects of human organ functions using in vitro (e.g. plates, transwells, etc.), in vivo (e.g. mouse, rat, etc.), or ex vivo (e.g. organ chips, 3D systems, etc.) organ models is of paramount importance for drug discovery and precision medicine. It will allow us to identify potential side effects and test the effectiveness of new therapeutic approaches early in their design phase, and will inform the development of better disease models. Developing mathematical methods to reliably compare the ‘distance/similarity’ of organ models from/to the real human organ they represent is an understudied problem with important applications in biomedicine and tissue engineering.

Results

We introduce the Transcriptomic Signature Distance (TSD), an information-theoretic distance for assessing the transcriptomic similarity of two tissue samples, or two groups of tissue samples. In developing TSD, we are leveraging next-generation sequencing data as well as information retrieved from well-curated databases providing signature gene sets characteristic for human organs. We present the justification and mathematical development of the new distance and demonstrate its effectiveness and advantages in different scenarios of practical importance using several publicly available RNA-seq datasets.

Availability and Implementation

The computation of both TSD versions (simple and weighted) has been implemented in R and can be downloaded from https://github.com/Cod3B3nd3R/Transcriptomic-Signature-Distance.

Contact

dimitris.manatakis@emulatebio.com

Supplementary information

Supplementary data are available at Bioinformatics online.

Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals

The global efforts in the past year have led to the discovery of nearly 200 drug repurposing candidates for COVID-19. Gaining more insights into their mechanisms of action could facilitate a better understanding of infection and the development of therapeutics. Leveraging large-scale drug-induced gene expression profiles, we found 36% of the active compounds regulate genes related to cholesterol homeostasis and microtubule cytoskeleton organization. Following bioinformatics analyses revealed that the expression of these genes is associated with COVID-19 patient severity and has predictive power on anti-SARS-CoV-2 efficacy in vitro. Monensin, a top new compound that regulates these genes, was further confirmed as an inhibitor of SARS-CoV-2 replication in Vero-E6 cells. Interestingly, drugs co-targeting cholesterol homeostasis and microtubule cytoskeleton organization processes more likely present a synergistic effect with antivirals. Therefore, potential therapeutics could be centered around combinations of targeting these processes and viral proteins.

Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions

The understanding of the mechanism-of-action (MoA) of compounds and the prediction of potential drug targets play an important role in small-molecule drug discovery. The aim of this work was to compare chemical and cell morphology information for bioactivity prediction. The comparison was performed using bioactivity data from the ExCAPE database, image data (in the form of CellProfiler features) from the Cell Painting data set (the largest publicly available data set of cell images with ∼30,000 compound perturbations), and extended connectivity fingerprints (ECFPs) using the multitask Bayesian matrix factorization (BMF) approach Macau. We found that the BMF Macau and random forest (RF) performance were overall similar when ECFPs were used as compound descriptors. However, BMF Macau outperformed RF in 159 out of 224 targets (71%) when image data were used as compound information. Using BMF Macau, 100 (corresponding to about 45%) and 90 (about 40%) of the 224 targets were predicted with high predictive performance (AUC > 0.8) with ECFP data and image data as side information, respectively. There were targets better predicted by image data as side information, such as β-catenin, and others better predicted by fingerprint-based side information, such as proteins belonging to the G-protein-Coupled Receptor 1 family, which could be rationalized from the underlying data distributions in each descriptor domain. In conclusion, both cell morphology changes and chemical structure information contain information about compound bioactivity, which is also partially complementary, and can hence contribute to in silico MoA analysis.

Identification of a New Chemotype of Anti-Obesity Compounds by Ensemble Screening

Despite the increasing prevalence of overweight or obesity in the global population, most of the approved drugs for obesity are still not ideal for long-term use due to severe cardiovascular and/or neurological side effects. Therefore, we designed a library-implemented virtual screening (VS) approach to discover new anti-obesity agents without significant toxicity. The Bayesian classification and 3D pharmacophore model for the VS process were built by using the screening results of our in-house library of natural piper amide-like compounds, which possess a wide range of biological activities and relatively low toxicities. The VS process identified six compounds of different classes with enhanced inhibitory activities against lipid accumulation and without toxicity. Moreover, the most active compound with an oxadiazole scaffold resulted in weight loss and improved the fatty liver condition of mice with overnutrition in animal experiments.

Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm

MOTIVATION

Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications.

RESULTS

Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for a range of human cell lines in the Library of Integrated Network-based Cellular Signatures. The results also revealed that in comparison with the use of original gene expression profiles, the use of imputed gene expression profiles improved the accuracy of drug repositioning. We also performed a comprehensive prediction of drug indications for diseases with gene expression profiles, which suggested many potential drug indications that were not predicted by previous approaches.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Systems Pharmacogenomic Landscape of Drug Similarities from LINCS data: Drug Association Networks

Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes.

Leveraging Big Data to Transform Drug Discovery

The surge of public disease and drug-related data availability has facilitated the application of computational methodologies to transform drug discovery. In the current chapter, we outline and detail the various resources and tools one can leverage in order to perform such analyses. We further describe in depth the in silico workflows of two recent studies that have identified possible novel indications of existing drugs. Lastly, we delve into the caveats and considerations of this process to enable other researchers to perform rigorous computational drug discovery experiments of their own.

Pathway-Based Drug Repositioning for Cancers: Computational Prediction and Experimental Validation

Developing drugs with anticancer activity and low toxic side-effects at low costs is a challenging issue for cancer chemotherapy. In this work, we propose to use molecular pathways as the therapeutic targets and develop a novel computational approach for drug repositioning for cancer treatment. We analyzed chemically induced gene expression data of 1112 drugs on 66 human cell lines and searched for drugs that inactivate pathways involved in the growth of cancer cells (cell cycle) and activate pathways that contribute to the death of cancer cells (e.g., apoptosis and p53 signaling). Finally, we performed a large-scale prediction of potential anticancer effects for all the drugs and experimentally validated the prediction results via three in vitro cellular assays that evaluate cell viability, cytotoxicity, and apoptosis induction. Using this strategy, we successfully identified several potential anticancer drugs. The proposed pathway-based method has great potential to improve drug repositioning research for cancer treatment.

Drug Repurposing Using Deep Embeddings of Gene Expression Profiles

Computational drug repositioning requires assessment of the functional similarities among compounds. Here, we report a new method for measuring compound functional similarity based on gene expression data. This approach takes advantage of deep neural networks to learn an embedding that substantially denoises expression data, making replicates of the same compound more similar. Our method uses unlabeled data in the sense that it only requires compounds to be labeled by identity rather than detailed pharmacological information, which is often unavailable and costly to obtain. Similarity in the learned embedding space accurately predicted pharmacological similarities despite the lack of any such labels during training and achieved substantially improved performance in comparison with previous similarity measures applied to gene expression measurements. Our method could identify drugs with shared therapeutic and biological targets even when the compounds were structurally dissimilar, thereby revealing previously unreported functional relationships between compounds. Thus, our approach provides an improved engine for drug repurposing based on expression data, which we have made available through the online tool DeepCodex ( http://deepcodex.org ).

Cardiovascular Disease Risk Varies by Birth Month in Canines

The canine heart is a robust physiological model for the human heart. Recently, birth month associations have been reported and replicated in humans using clinical health records. While animals respond readily to their environment in the wild, a systematic investigation of birth season dependencies among pets and specifically canines remains lacking. We obtained data from the Orthopedic Foundation of Animals on 129,778 canines representing 253 distinct breeds. Among canines that were not predisposed to cardiovascular disease, a clear birth season relationship is observed with peak risk occurring in June-August. Our findings indicate that acquired cardiovascular disease among canines, especially those that are not predisposed to cardiovascular disease, appears birth season dependent. The relative risk of cardiovascular disease for canines not predisposed to cardiovascular disease was as high as 1.47 among July pups. The overall adjusted odds ratio, when mixed breeds were excluded, for the birth season effect was 1.02 (95% CI: 1.002, 1.047, p = 0.032) after adjusting for breed and genetic cardiovascular predisposition effects. Studying birth season effects in model organisms can help to elucidate potential mechanisms behind the reported associations.

Model-Based Clustering With Data Correction For Removing Artifacts In Gene Expression Data

The NIH Library of Integrated Network-based Cellular Signatures (LINCS) contains gene expression data from over a million experiments, using Luminex Bead technology. Only 500 colors are used to measure the expression levels of the 1,000 landmark genes measured, and the data for the resulting pairs of genes are deconvolved. The raw data are sometimes inadequate for reliable deconvolution, leading to artifacts in the final processed data. These include the expression levels of paired genes being flipped or given the same value, and clusters of values that are not at the true expression level. We propose a new method called model-based clustering with data correction (MCDC) that is able to identify and correct these three kinds of artifacts simultaneously. We show that MCDC improves the resulting gene expression data in terms of agreement with external baselines, as well as improving results from subsequent analysis.

Rational screening of peroxisome proliferator-activated receptor-γ agonists from natural products: potential therapeutics for heart failure

CONTEXT

Peroxisome proliferator-activated receptor-γ (PPARγ) is a member of the nuclear hormone receptor superfamily of ligand-activated transcription factors. Activation of PPARγ pathway has been shown to enhance fatty acid oxidation, improve endothelial cell function, and decrease myocardial fibrosis in heart failure. Thus, the protein has been raised as an attractive target for heart failure therapy.

OBJECTIVE

This work attempted to discover new and potent PPARγ agonists from natural products using a synthetic strategy of computer virtual screening and transactivation reporter assay.

MATERIALS AND METHODS

A large library of structurally diverse, drug-like natural products was compiled, from which those with unsatisfactory pharmacokinetic profile and/or structurally redundant compounds were excluded. The binding mode of remaining candidates to PPARγ ligand-binding domain (LBD) was computationally modelled using molecular docking and their relative binding potency was ranked by an empirical scoring scheme. Consequently, eight commercially available hits with top scores were selected and their biological activity was determined using a cell-based reporter-gene assay.

RESULTS

Four natural product compounds, namely ZINC13408172, ZINC4292805, ZINC44179 and ZINC901461, were identified to have high or moderate agonistic potency against human PPARγ with EC₅₀ values of 0.084, 2.1, 0.35 and 5.6 μM, respectively, which are comparable to or even better than that of the approved PPARγ full agonists pioglitazone (EC₅₀=0.16 μM) and rosiglitazone (EC₅₀=0.034 μM). Hydrophobic interactions and van der Waals contacts are the primary chemical forces to stabilize the complex architecture of PPARγ LBD domain with these agonist ligands, while few hydrogen bonds, salt bridges and/or π-π stacking at the complex interfaces confer selectivity and specificity for the domain-agonist recognition.

DISCUSSION AND CONCLUSION

The integrated in vitro-in silico screening strategy can be successfully applied to rational discovery of biologically active compounds. The newly identified natural products with PPARγ agonistic potency are considered as promising lead scaffolds to develop novel chemical therapeutics for heart failure.

Combined inhibition of atypical PKC and histone deacetylase 1 is cooperative in basal cell carcinoma treatment

Advanced basal cell carcinomas (BCCs) circumvent Smoothened (SMO) inhibition by activating GLI transcription factors to sustain the high levels of Hedgehog (HH) signaling required for their survival. Unfortunately, there is a lack of efficacious therapies. We performed a gene expression-based drug repositioning screen in silico and identified the FDA-approved histone deacetylase (HDAC) inhibitor, vorinostat, as a top therapeutic candidate. We show that vorinostat only inhibits proliferation of BCC cells in vitro and BCC allografts in vivo at high dose, limiting its usefulness as a monotherapy. We leveraged this in silico approach to identify drug combinations that increase the therapeutic window of vorinostat and identified atypical PKC Ɩ/ʎ (aPKC) as a HDAC costimulator of HH signaling. We found that aPKC promotes GLI1-HDAC1 association in vitro, linking two positive feedback loops. Combination targeting of HDAC1 and aPKC robustly inhibited GLI1, lowering drug doses needed in vitro, in vivo, and ex vivo in patient-derived BCC explants. We identified a bioavailable and selective small-molecule aPKC inhibitor, bringing the pharmacological blockade of aPKC and HDAC1 into the realm of clinical possibility. Our findings provide a compelling rationale and candidate drugs for combined targeting of HDAC1 and aPKC in HH-dependent cancers.

Molecular Structure-Based Large-Scale Prediction of Chemical-Induced Gene Expression Changes

The quantitative structure-activity relationship (QSAR) approach has been used to model a wide range of chemical-induced biological responses. However, it had not been utilized to model chemical-induced genomewide gene expression changes until very recently, owing to the complexity of training and evaluating a very large number of models. To address this issue, we examined the performance of a variable nearest neighbor (v-NN) method that uses information on near neighbors conforming to the principle that similar structures have similar activities. Using a data set of gene expression signatures of 13 150 compounds derived from cell-based measurements in the NIH Library of Integrated Network-based Cellular Signatures program, we were able to make predictions for 62% of the compounds in a 10-fold cross validation test, with a correlation coefficient of 0.61 between the predicted and experimentally derived signatures-a reproducibility rivaling that of high-throughput gene expression measurements. To evaluate the utility of the predicted gene expression signatures, we compared the predicted and experimentally derived signatures in their ability to identify drugs known to cause specific liver, kidney, and heart injuries. Overall, the predicted and experimentally derived signatures had similar receiver operating characteristics, whose areas under the curve ranged from 0.71 to 0.77 and 0.70 to 0.73, respectively, across the three organ injury models. However, detailed analyses of enrichment curves indicate that signatures predicted from multiple near neighbors outperformed those derived from experiments, suggesting that averaging information from near neighbors may help improve the signal from gene expression measurements. Our results demonstrate that the v-NN method can serve as a practical approach for modeling large-scale, genomewide, chemical-induced, gene expression changes.

Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets

The decreasing cost of genomic technologies has enabled the molecular characterization of large-scale clinical disease samples and of molecular changes upon drug treatment in various disease models. Exploring methods to relate diseases to potentially efficacious drugs through various molecular features is critically important in the discovery of new therapeutics. Here we show that the potency of a drug to reverse cancer-associated gene expression changes positively correlates with that drug's efficacy in preclinical models of breast, liver and colon cancers. Using a systems-based approach, we predict four compounds showing high potency to reverse gene expression in liver cancer and validate that all four compounds are effective in five liver cancer cell lines. The in vivo efficacy of pyrvinium pamoate is further confirmed in a subcutaneous xenograft model. In conclusion, this systems-based approach may be complementary to the traditional target-based approach in connecting diseases to potentially efficacious drugs.

Computational Discovery of Niclosamide Ethanolamine, a Repurposed Drug Candidate That Reduces Growth of Hepatocellular Carcinoma Cells In Vitro and in Mice by Inhibiting Cell Division Cycle 37 Signaling

BACKGROUND & AIMS

Drug repositioning offers a shorter approval process than new drug development. We therefore searched large public datasets of drug-induced gene expression signatures to identify agents that might be effective against hepatocellular carcinoma (HCC).

METHODS

We searched public databases of messenger RNA expression patterns reported from HCC specimens from patients, HCC cell lines, and cells exposed to various drugs. We identified drugs that might specifically increase expression of genes that are down-regulated in HCCs and reduce expression of genes up-regulated in HCCs using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. We evaluated the anti-tumor activity of niclosamide and its ethanolamine salt (NEN) in HCC cell lines (HepG2, Huh7, Hep3B, Hep40, and PLC/PRF/5), primary human hepatocytes, and 2 mouse models of HCC. In one model of HCC, liver tumor development was induced by hydrodynamic delivery of a sleeping beauty transposon expressing an activated form of Ras (v12) and truncated β-catenin (N90). In another mouse model, patient-derived xenografts were established by implanting HCC cells from patients into livers of immunocompromised mice. Tumor growth was monitored by bioluminescence imaging. Tumor-bearing mice were fed a regular chow diet or a chow diet containing niclosamide or NEN. In a separate experiment using patient-derived xenografts, tumor-bearing mice were given sorafenib (the standard of care for patients with advanced HCC), NEN, or niclosamide alone; a combination of sorafenib and NEN; or a combination sorafenib and niclosamide in their drinking water, or regular water (control), and tumor growth was monitored.

RESULTS

Based on gene expression signatures, we identified 3 anthelmintics that significantly altered the expression of genes that are up- or down-regulated in HCCs. Niclosamide and NEN specifically reduced the viability of HCC cells: the agents were at least 7-fold more cytotoxic to HCCs than primary hepatocytes. Oral administration of NEN to mice significantly slowed growth of genetically induced liver tumors and patient-derived xenografts, whereas niclosamide did not, coinciding with the observed greater bioavailability of NEN compared with niclosamide. The combination of NEN and sorafenib was more effective at slowing growth of patient-derived xenografts than either agent alone. In HepG2 cells and in patient-derived xenografts, administration of niclosamide or NEN increased expression of 20 genes down-regulated in HCC and reduced expression of 29 genes up-regulated in the 274-gene HCC signature. Administration of NEN to mice with patient-derived xenografts reduced expression of proteins in the Wnt-β-catenin, signal transducer and activator of transcription 3, AKT-mechanistic target of rapamycin, epidermal growth factor receptor-Ras-Raf signaling pathways. Using immunoprecipitation assays, we found NEN to bind cell division cycle 37 protein and disrupt its interaction with heat shock protein 90.

CONCLUSIONS

In a bioinformatics search for agents that alter the HCC-specific gene expression pattern, we identified the anthelmintic niclosamide as a potential anti-tumor agent. Its ethanolamine salt, with greater bioavailability, was more effective than niclosamide at slowing the growth of genetically induced liver tumors and patient-derived xenografts in mice. Both agents disrupted interaction between cell division cycle 37 and heat shock protein 90 in HCC cells, with concomitant inhibition of their downstream signaling pathways. NEN might be effective for treatment of patients with HCC.

Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy

Identification of drug targets and mechanism of action (MoA) for new and uncharacterized anticancer drugs is important for optimization of treatment efficacy. Current MoA prediction largely relies on prior information including side effects, therapeutic indication, and chemoinformatics. Such information is not transferable or applicable for newly identified, previously uncharacterized small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary toward development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose here a new integrative computational pharmacogenomic approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that rely only on basic drug characteristics toward elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation, and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. DNF taxonomy succeeded in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. The scalability of DNF facilitated identification of key drug relationships across different drug categories, providing a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on compound MoA and potential insights for drug repurposing. Cancer Res; 77(11); 3057-69. ©2017 AACR.

Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics

The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical-protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.

Integrating Clinical Phenotype and Gene Expression Data to Prioritize Novel Drug Uses

Drug repositioning has been based largely on genomic signatures of drugs and diseases. One challenge in these efforts lies in connecting the molecular signatures of drugs into clinical responses, including therapeutic and side effects, to the repurpose of drugs. We addressed this challenge by evaluating drug‐drug relationships using a phenotypic and molecular‐based approach that integrates therapeutic indications, side effects, and gene expression profiles induced by each drug. Using cosine similarity, relationships between 445 drugs were evaluated based on high‐dimensional spaces consisting of phenotypic terms of drugs and genomic signatures, respectively. One hundred fifty‐one of 445 drugs comprising 450 drug pairs displayed significant similarities in both phenotypic and genomic signatures (P value < 0.05). We also found that similar gene expressions of drugs do indeed yield similar clinical phenotypes. We generated similarity matrixes of drugs using the expression profiles they induce in a cell line and phenotypic effects.

Combined QSAR-based virtual screening and fluorescence binding assay to identify natural product mediators of Interferon Regulatory Factor 7 (IRF-7) in pulmonary infection

Interferon regulatory factor-7 (IRF-7) is involved in pulmonary infection and pneumonia. Here, a synthetic strategy that combined quantitative structure-activity relationship (QSAR)-based virtual screening and in vitro binding assay was described to identify new and potent mediator ligands of IRF-7 from natural products. In the procedure, a QSAR scoring function was developed and validated using Gaussian process (GP) regression and a structure-based set of protein-ligand affinity data. By integrating hotspot pocket prediction, pharmacokinetics profile analysis and molecular docking calculations, the scoring function was successfully applied to virtual screening against a large library of structurally diverse, drug-like natural products. With the method we were able to identify a number of potential hits, from which several compounds were found to have moderate or high affinity to IRF-7 using fluorescence binding assays, with dissociation constants K_d at micromolar level. We have also examined the structural basis and noncovalent interactions of computationally modelled IRF-7 complex with its potent ligands. It is revealed that hydrophobic forces and van der Waals contacts play a central role in stabilization of the complex architecture, while few hydrogen bonds confer additional specificity for the protein-ligand recognition.

Drug combination therapy increases successful drug repositioning

Repositioning of approved drugs has recently gained new momentum for rapid identification and development of new therapeutics for diseases that lack effective drug treatment. Reported repurposing screens have increased dramatically in number in the past five years. However, many newly identified compounds have low potency; this limits their immediate clinical applications because the known, tolerated plasma drug concentrations are lower than the required therapeutic drug concentrations. Drug combinations of two or more compounds with different mechanisms of action are an alternative approach to increase the success rate of drug repositioning.

In silico methods for drug repurposing and pharmacology

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.

Prediction of Candidate Drugs for Treating Pancreatic Cancer by Using a Combined Approach

Pancreatic cancer is the leading cause of death from solid malignancies worldwide. Currently, gemcitabine is the only drug approved for treating pancreatic cancer. Developing new therapeutic drugs for this disease is, therefore, an urgent need. The C-Map project has provided a wealth of gene expression data that can be mined for repositioning drugs, a promising approach to new drug discovery. Typically, a drug is considered potentially useful for treating a disease if the drug-induced differential gene expression profile is negatively correlated with the differentially expressed genes in the target disease. However, many of the potentially useful drugs (PUDs) identified by gene expression profile correlation are likely false positives because, in C-Map, the cultured cell lines to which the drug is applied are not derived from diseased tissues. To solve this problem, we developed a combined approach for predicting candidate drugs for treating pancreatic cancer. We first identified PUDs for pancreatic cancer by using C-Map-based gene expression correlation analyses. We then applied an algorithm (Met-express) to predict key pancreatic cancer (KPC) enzymes involved in pancreatic cancer metabolism. Finally, we selected candidates from the PUDs by requiring that their targets be KPC enzymes or the substrates/products of KPC enzymes. Using this combined approach, we predicted seven candidate drugs for treating pancreatic cancer, three of which are supported by literature evidence, and three were experimentally validated to be inhibitory to pancreatic cancer celllines.