Connecting genetics and gene expression data for target prioritisation and drug repositioning

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.

Integrated Genomic Profiling and Drug Screening of Patient-Derived Cultures Identifies Individualized Copy Number-Dependent Susceptibilities Involving PI3K Pathway and 17q Genes in Neuroblastoma

Neuroblastoma is the commonest extracranial pediatric malignancy. With few recurrent single nucleotide variations (SNVs), mutation-based precision oncology approaches have limited utility, but its frequent and heterogenous copy number variations (CNVs) could represent genomic dependencies that may be exploited for personalized therapy. Patient-derived cell culture (PDC) models can facilitate rapid testing of multiple agents to determine such individualized drug-responses. Thus, to study the relationship between individual genomic aberrations and therapeutic susceptibilities, we integrated comprehensive genomic profiling of neuroblastoma tumors with drug screening of corresponding PDCs against 418 targeted inhibitors. We quantified the strength of association between copy number and cytotoxicity, and validated significantly correlated gene-drug pairs in public data and using machine learning models. Somatic mutations were infrequent (3.1 per case), but copy number losses in 1p (31%) and 11q (38%), and gains in 17q (69%) were prevalent. Critically, in-vitro cytotoxicity significantly correlated only with CNVs, but not SNVs. Among 1278 significantly correlated gene-drug pairs, copy number of GNA13 and DNA damage response genes CBL, DNMT3A, and PPM1D were most significantly correlated with cytotoxicity; the drugs most commonly associated with these genes were PI3K/mTOR inhibitor PIK-75, and CDK inhibitors P276-00, SNS-032, AT7519, flavopiridol and dinaciclib. Predictive Markov random field models constructed from CNVs alone recapitulated the true z-score-weighted associations, with the strongest gene-drug functional interactions in subnetworks involving PI3K and JAK-STAT pathways. Together, our data defined individualized dose-dependent relationships between copy number gains of PI3K and STAT family genes particularly on 17q and susceptibility to PI3K and cell cycle agents in neuroblastoma. Integration of genomic profiling and drug screening of patient-derived models of neuroblastoma can quantitatively define copy number-dependent sensitivities to targeted inhibitors, which can guide personalized therapy for such mutationally quiet cancers.

Understanding Drug Repurposing From the Perspective of Biomedical Entities and Their Evolution: Bibliographic Research Using Aspirin

BACKGROUND

Drug development is still a costly and time-consuming process with a low rate of success. Drug repurposing (DR) has attracted significant attention because of its significant advantages over traditional approaches in terms of development time, cost, and safety. Entitymetrics, defined as bibliometric indicators based on biomedical entities (eg, diseases, drugs, and genes) studied in the biomedical literature, make it possible for researchers to measure knowledge evolution and the transfer of drug research.

OBJECTIVE

The purpose of this study was to understand DR from the perspective of biomedical entities (diseases, drugs, and genes) and their evolution.

METHODS

In the work reported in this paper, we extended the bibliometric indicators of biomedical entities mentioned in PubMed to detect potential patterns of biomedical entities in various phases of drug research and investigate the factors driving DR. We used aspirin (acetylsalicylic acid) as the subject of the study since it can be repurposed for many applications. We propose 4 easy, transparent measures based on entitymetrics to investigate DR for aspirin: Popularity Index (P₁), Promising Index (P₂), Prestige Index (P₃), and Collaboration Index (CI).

RESULTS

We found that the maxima of P₁, P₃, and CI are closely associated with the different repurposing phases of aspirin. These metrics enabled us to observe the way in which biomedical entities interacted with the drug during the various phases of DR and to analyze the potential driving factors for DR at the entity level. P₁ and CI were indicative of the dynamic trends of a specific biomedical entity over a long time period, while P₂ was more sensitive to immediate changes. P₃ reflected the early signs of the practical value of biomedical entities and could be valuable for tracking the research frontiers of a drug.

CONCLUSIONS

In-depth studies of side effects and mechanisms, fierce market competition, and advanced life science technologies are driving factors for DR. This study showcases the way in which researchers can examine the evolution of DR using entitymetrics, an approach that can be valuable for enhancing decision making in the field of drug discovery and development.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

PS4DR: a multimodal workflow for identification and prioritization of drugs based on pathway signatures

Background

During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning.

Results

Here, we present a customizable workflow (PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs.

Conclusion

We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr.

Pathway and network embedding methods for prioritizing psychiatric drugs

One in five Americans experience mental illness, and roughly 75% of psychiatric prescriptions do not successfully treat the patient's condition. Extensive evidence implicates genetic factors and signaling disruption in the pathophysiology of these diseases. Changes in transcription often underlie this molecular pathway dysregulation; individual patient transcriptional data can improve the efficacy of diagnosis and treatment. Recent large-scale genomic studies have uncovered shared genetic modules across multiple psychiatric disorders - providing an opportunity for an integrated multi-disease approach for diagnosis. Moreover, network-based models informed by gene expression can represent pathological biological mechanisms and suggest new genes for diagnosis and treatment. Here, we use patient gene expression data from multiple studies to classify psychiatric diseases, integrate knowledge from expert-curated databases and publicly available experimental data to create augmented disease-specific gene sets, and use these to recommend disease-relevant drugs. From Gene Expression Omnibus, we extract expression data from 145 cases of schizophrenia, 82 cases of bipolar disorder, 190 cases of major depressive disorder, and 307 shared controls. We use pathway-based approaches to predict psychiatric disease diagnosis with a random forest model (78% accuracy) and derive important features to augment available drug and disease signatures. Using protein-protein-interaction networks and embedding-based methods, we build a pipeline to prioritize treatments for psychiatric diseases that achieves a 3.4-fold improvement over a background model. Thus, we demonstrate that gene-expression-derived pathway features can diagnose psychiatric diseases and that molecular insights derived from this classification task can inform treatment prioritization for psychiatric diseases.

Single Cell and Single Nucleus RNA-Seq Reveal Cellular Heterogeneity and Homeostatic Regulatory Networks in Adult Mouse Stria Vascularis

The stria vascularis (SV) generates the endocochlear potential (EP) in the inner ear and is necessary for proper hair cell mechanotransduction and hearing. While channels belonging to SV cell types are known to play crucial roles in EP generation, relatively little is known about gene regulatory networks that underlie the ability of the SV to generate and maintain the EP. Using single cell and single nucleus RNA-sequencing, we identify and validate known and rare cell populations in the SV. Furthermore, we establish a basis for understanding molecular mechanisms underlying SV function by identifying potential gene regulatory networks as well as druggable gene targets. Finally, we associate known deafness genes with adult SV cell types. This work establishes a basis for dissecting the genetic mechanisms underlying the role of the SV in hearing and will serve as a basis for designing therapeutic approaches to hearing loss related to SV dysfunction.

An omics perspective on drug target discovery platforms

The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

Insights into Computational Drug Repurposing for Neurodegenerative Disease

Computational drug repurposing has the ability to remarkably reduce drug development time and cost in an era where these factors are prohibitively high. Several examples of successful repurposed drugs exist in fields such as oncology, diabetes, leprosy, inflammatory bowel disease, among others, however computational drug repurposing in neurodegenerative disease has presented several unique challenges stemming from the lack of validation methods and difficulty in studying heterogenous diseases of aging. Here, we examine existing approaches to computational drug repurposing, including molecular, clinical, and biophysical methods, and propose data sources and methods to advance computational drug repurposing in neurodegenerative disease using Alzheimer's disease as an example.

Repositioning of Anti-parasitic Drugs in Cyclodextrin Inclusion Complexes for Treatment of Triple-Negative Breast Cancer

Drug repositioning refers to the identification of new therapeutic indications for drugs already approved. Albendazole and ricobendazole have been used as anti-parasitic drugs for many years; their therapeutic action is based on the inhibition of microtubule formation. Therefore, the study of their properties as antitumor compounds and the design of an appropriate formulation for cancer therapy is an interesting issue to investigate. The selected compounds are poorly soluble in water, and consequently, they have low and erratic bioavailability. In order to improve their biopharmaceutics properties, several formulations employing cyclodextrin inclusion complexes were developed. To carefully evaluate the in vitro and in vivo antitumor activity of these drugs and their complexes, several studies were performed on a breast cancer cell line (4T1) and BALB/c mice. In vitro studies showed that albendazole presented improved antitumor activity compared with ricobendazole. Furthermore, albendazole:citrate-β-cyclodextrin complex decreased significantly 4T1 cell growth both in in vitro and in vivo experiments. Thus, new formulations for anti-parasitic drugs could help to reposition them for new therapeutic indications, offering safer and more effective treatments by using a well-known drug.