Literature mining, ontologies and information visualization for drug repurposing

Non-Negative Matrix Tri-Factorization for Representation Learning in Multi-Omics Datasets with Applications to Drug Repurposing and Selection

The vast corpus of heterogeneous biomedical data stored in databases, ontologies, and terminologies presents a unique opportunity for drug design. Integrating and fusing these sources is essential to develop data representations that can be analyzed using artificial intelligence methods to generate novel drug candidates or hypotheses. Here, we propose Non-Negative Matrix Tri-Factorization as an invaluable tool for integrating and fusing data, as well as for representation learning. Additionally, we demonstrate how representations learned by Non-Negative Matrix Tri-Factorization can effectively be utilized by traditional artificial intelligence methods. While this approach is domain-agnostic and applicable to any field with vast amounts of structured and semi-structured data, we apply it specifically to computational pharmacology and drug repurposing. This field is poised to benefit significantly from artificial intelligence, particularly in personalized medicine. We conducted extensive experiments to evaluate the performance of the proposed method, yielding exciting results, particularly compared to traditional methods. Novel drug-target predictions have also been validated in the literature, further confirming their validity. Additionally, we tested our method to predict drug synergism, where constructing a classical matrix dataset is challenging. The method demonstrated great flexibility, suggesting its applicability to a wide range of tasks in drug design and discovery.

Enhancing the coverage of SemRep using a relation classification approach

OBJECTIVE

Relation extraction is an essential task in the field of biomedical literature mining and offers significant benefits for various downstream applications, including database curation, drug repurposing, and literature-based discovery. The broad-coverage natural language processing (NLP) tool SemRep has established a solid baseline for extracting subject-predicate-object triples from biomedical text and has served as the backbone of the Semantic MEDLINE Database (SemMedDB), a PubMed-scale repository of semantic triples. While SemRep achieves reasonable precision (0.69), its recall is relatively low (0.42). In this study, we aimed to enhance SemRep using a relation classification approach, in order to eventually increase the size and the utility of SemMedDB.

METHODS

We combined and extended existing SemRep evaluation datasets to generate training data. We leveraged the pre-trained PubMedBERT model, enhancing it through additional contrastive pre-training and fine-tuning. We experimented with three entity representations: mentions, semantic types, and semantic groups. We evaluated the model performance on a portion of the SemRep Gold Standard dataset and compared it to SemRep performance. We also assessed the effect of the model on a larger set of 12K randomly selected PubMed abstracts.

RESULTS

Our results show that the best model yields a precision of 0.62, recall of 0.81, and F1 score of 0.70. Assessment on 12K abstracts shows that the model could double the size of SemMedDB, when applied to entire PubMed. We also manually assessed the quality of 506 triples predicted by the model that SemRep had not previously identified, and found that 67% of these triples were correct.

CONCLUSION

These findings underscore the promise of our model in achieving a more comprehensive coverage of relationships mentioned in biomedical literature, thereby showing its potential in enhancing various downstream applications of biomedical literature mining. Data and code related to this study are available at https://github.com/Michelle-Mings/SemRep_RelationClassification.

DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm

Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease-drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method's feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease-drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.

System biology approaches for drug repurposing

Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.

A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks

Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.

Anti-Tuberculosis Mur Inhibitors: Structural Insights and the Way Ahead for Development of Novel Agents

Mur enzymes serve as critical molecular devices for the synthesis of UDP-MurNAc-pentapeptide, the main building block of bacterial peptidoglycan polymer. These enzymes have been extensively studied for bacterial pathogens such as Escherichia coli and Staphylococcus aureus. Various selective and mixed Mur inhibitors have been designed and synthesized in the past few years. However, this class of enzymes remains relatively unexplored for Mycobacterium tuberculosis (Mtb), and thus offers a promising approach for drug design to overcome the challenges of battling this global pandemic. This review aims to explore the potential of Mur enzymes of Mtb by systematically scrutinizing the structural aspects of various reported bacterial inhibitors and implications concerning their activity. Diverse chemical scaffolds such as thiazolidinones, pyrazole, thiazole, etc., as well as natural compounds and repurposed compounds, have been reviewed to understand their in silico interactions with the receptor or their enzyme inhibition potential. The structural diversity and wide array of substituents indicate the scope of the research into developing varied analogs and providing valuable information for the purpose of modifying reported inhibitors of other multidrug-resistant microorganisms. Therefore, this provides an opportunity to expand the arsenal against Mtb and overcome multidrug-resistant tuberculosis.

DRONet: effectiveness-driven drug repositioning framework using network embedding and ranking learning

As one of the most vital methods in drug development, drug repositioning emphasizes further analysis and research of approved drugs based on the existing large amount of clinical and experimental data to identify new indications of drugs. However, the existing drug repositioning methods didn't achieve enough prediction performance, and these methods do not consider the effectiveness information of drugs, which make it difficult to obtain reliable and valuable results. In this study, we proposed a drug repositioning framework termed DRONet, which make full use of effectiveness comparative relationships (ECR) among drugs as prior information by combining network embedding and ranking learning. We utilized network embedding methods to learn the deep features of drugs from a heterogeneous drug-disease network, and constructed a high-quality drug-indication data set including effectiveness-based drug contrast relationships. The embedding features and ECR of drugs are combined effectively through a designed ranking learning model to prioritize candidate drugs. Comprehensive experiments show that DRONet has higher prediction accuracy (improving 87.4% on Hit@1 and 37.9% on mean reciprocal rank) than state of the art. The case analysis also demonstrates high reliability of predicted results, which has potential to guide clinical drug development.

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.

Drug repositioning: A bibliometric analysis

Drug repurposing has become an effective approach to drug discovery, as it offers a new way to explore drugs. Based on the Science Citation Index Expanded (SCI-E) and Social Sciences Citation Index (SSCI) databases of the Web of Science core collection, this study presents a bibliometric analysis of drug repurposing publications from 2010 to 2020. Data were cleaned, mined, and visualized using Derwent Data Analyzer (DDA) software. An overview of the history and development trend of the number of publications, major journals, major countries, major institutions, author keywords, major contributors, and major research fields is provided. There were 2,978 publications included in the study. The findings show that the United States leads in this area of research, followed by China, the United Kingdom, and India. The Chinese Academy of Science published the most research studies, and NIH ranked first on the h-index. The Icahn School of Medicine at Mt Sinai leads in the average number of citations per study. Sci Rep, Drug Discov. Today, and Brief. Bioinform. are the three most productive journals evaluated from three separate perspectives, and pharmacology and pharmacy are unquestionably the most commonly used subject categories. Cheng, FX; Mucke, HAM; and Butte, AJ are the top 20 most prolific and influential authors. Keyword analysis shows that in recent years, most research has focused on drug discovery/drug development, COVID-19/SARS-CoV-2/coronavirus, molecular docking, virtual screening, cancer, and other research areas. The hotspots have changed in recent years, with COVID-19/SARS-CoV-2/coronavirus being the most popular topic for current drug repurposing research.

Literature-based drug-drug similarity for drug repurposing: impact of Medical Subject Headings term refinement and hierarchical clustering

Background: We describe herein, an improved procedure for drug repurposing based on refined Medical Subject Headings (MeSH) terms and hierarchical clustering method. Materials & methods: In the present study, we have employed MeSH data from MEDLINE (2019), 1669 US FDA approved drugs from Open FDA and a refined set of MeSH terms. Refinement of MeSH terms was performed to include terms related to mechanistic information of drugs and diseases. Results and Conclusions: In-depth analysis of the results obtained, demonstrated greater efficiency of the proposed approach, based on refined MeSH terms and hierarchical clustering, in terms of number of selected drug candidates for repurposing. Further, analysis of misclustering and size of noise clusters suggest that the proposed approach is reliable and can be employed in drug repurposing.

Sildenafil, a Type-5 Phosphodiesterase Inhibitor, Fails to Reverse Myeloid-Derived Suppressor Cell-Mediated T Cell Suppression in Cells Isolated From Tuberculosis Patients

Successful TB treatment is hampered by increasing resistance to the two most effective first-line anti-TB drugs, namely isoniazid and rifampicin, thus innovative therapies focused on host processes, termed host-directed therapies (HDTs), are promising novel approaches for increasing treatment efficacy without inducing drug resistance. We assessed the ability of Sildenafil, a type-5 phosphodiesterase inhibitor, as a repurposed compound, to serve as HDT target, by counteracting the suppressive effects of myeloid-derived suppressor cells (MDSC) obtained from active TB cases on T-cell responsiveness. We confirm that MDSC suppress non-specific T-cell activation. We also show that Sildenafil treatment fails to reverse the MDSC-mediated suppression of T-cell functions measured here, namely activation and proliferation. The impact of Sildenafil treatment on improved immunity, using the concentration tested here, is likely to be minimal, but further identification and development of MDSC-targeting TB host-directed therapies are warranted.

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.

Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications

Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normalization before applying a machine learning model that utilizes the combination of graph and literature. We then use supervised machine learning to show the effects of combining features from biomedical knowledge and published literature on the prediction of drug targets and drug indications. We demonstrate that our approach using datasets for drug-target interactions and drug indications is scalable to large graphs and can be used to improve the ranking of targets and indications by exploiting features from either structure or unstructured information alone.

Considerations and challenges for sex-aware drug repurposing

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

Text Mining-Based Drug Discovery for Connective Tissue Disease–Associated Pulmonary Arterial Hypertension

Background: The current medical treatments for connective tissue disease–associated pulmonary arterial hypertension (CTD-PAH) do not show favorable efficiency for all patients, and identification of novel drugs is desired.

Methods: Text mining was performed to obtain CTD- and PAH-related gene sets, and the intersection of the two gene sets was analyzed for functional enrichment through DAVID. The protein–protein interaction network of the overlapping genes and the significant gene modules were determined using STRING. The enriched candidate genes were further analyzed by Drug Gene Interaction database to identify drugs with potential therapeutic effects on CTD-PAH.

Results: Based on text mining analysis, 179 genes related to CTD and PAH were identified. Through enrichment analysis of the genes, 20 genes representing six pathways were obtained. To further narrow the scope of potential existing drugs, we selected targeted drugs with a Query Score ≥5 and Interaction Score ≥1. Finally, 13 drugs targeting the six genes were selected as candidate drugs, which were divided into four drug–gene interaction types, and 12 of them had initial drug indications approved by the FDA. The potential gene targets of the drugs on this list are IL-6 (one drug) and IL-1β (two drugs), MMP9 (one drug), VEGFA (three drugs), TGFB1 (one drug), and EGFR (five drugs). These drugs might be used to treat CTD-PAH.

Conclusion: We identified 13 drugs targeting six genes that may have potential therapeutic effects on CTD-PAH.

Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method

Indication expansion aims to find new indications for existing targets in order to accelerate the process of launching a new drug for a disease on the market. The rapid increase in data types and data sources for computational drug discovery has fostered the use of semantic knowledge graphs (KGs) for indication expansion through target centric approaches, or in other words, target repositioning. Previously, we developed a novel method to construct a KG for indication expansion studies, with the aim of finding and justifying alternative indications for a target gene of interest. In contrast to other KGs, ours combines human-curated full-text literature and gene expression data from biomedical databases to encode relationships between genes, diseases, and tissues. Here, we assessed the suitability of our KG for explainable target-disease link prediction using a glass-box approach. To evaluate the predictive power of our KG, we applied shortest path with tissue information- and embedding-based prediction methods to a graph constructed with information published before or during 2010. We also obtained random baselines by applying the shortest path predictive methods to KGs with randomly shuffled node labels. Then, we evaluated the accuracy of the top predictions using gene-disease links reported after 2010. In addition, we investigated the contribution of the KG’s tissue expression entity to the prediction performance. Our experiments showed that shortest path-based methods significantly outperform the random baselines and embedding-based methods outperform the shortest path predictions. Importantly, removing the tissue expression entity from the KG severely impacts the quality of the predictions, especially those produced by the embedding approaches. Finally, since the interpretability of the predictions is crucial in indication expansion, we highlight the advantages of our glass-box model through the examination of example candidate target-disease predictions.

DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer's Disease

Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantial global healthcare cost. Unfortunately, current treatments are only palliative and do not cure AD. There is an urgent need to develop novel anti-AD therapies; however, drug discovery is a time-consuming, expensive, and high-risk process. Drug repositioning, on the other hand, is an attractive approach to identify drugs for AD treatment. Thus, we developed a novel deep learning method called DOTA (Drug repositioning approach using Optimal Transport for Alzheimer's disease) to repurpose effective FDA-approved drugs for AD. Specifically, DOTA consists of two major autoencoders: (1) a multi-modal autoencoder to integrate heterogeneous drug information and (2) a Wasserstein variational autoencoder to identify effective AD drugs. Using our approach, we predict that antipsychotic drugs with circadian effects, such as quetiapine, aripiprazole, risperidone, suvorexant, brexpiprazole, olanzapine, and trazadone, will have efficacious effects in AD patients. These drugs target important brain receptors involved in memory, learning, and cognition, including serotonin 5-HT2A, dopamine D2, and orexin receptors. In summary, DOTA repositions promising drugs that target important biological pathways and are predicted to improve patient cognition, circadian rhythms, and AD pathogenesis.

Ontology-based identification and prioritization of candidate drugs for epilepsy from literature

Background

Drug repurposing can improve the return of investment as it finds new uses for existing drugs. Literature-based analyses exploit factual knowledge on drugs and diseases, e.g. from databases, and combine it with information from scholarly publications. Here we report the use of the Open Discovery Process on scientific literature to identify non-explicit ties between a disease, namely epilepsy, and known drugs, making full use of available epilepsy-specific ontologies.

Results

We identified characteristics of epilepsy-specific ontologies to create subsets of documents from the literature; from these subsets we generated ranked lists of co-occurring neurological drug names with varying specificity. From these ranked lists, we observed a high intersection regarding reference lists of pharmaceutical compounds recommended for the treatment of epilepsy. Furthermore, we performed a drug set enrichment analysis, i.e. a novel scoring function using an adaptive tuning parameter and comparing top-k ranked lists taking into account the varying length and the current position in the list. We also provide an overview of the pharmaceutical space in the context of epilepsy, including a final combined ranked list of more than 70 drug names.

Conclusions

Biomedical ontologies are a rich resource that can be combined with text mining for the identification of drug names for drug repurposing in the domain of epilepsy. The ranking of the drug names related to epilepsy provides benefits to patients and to researchers as it enables a quick evaluation of statistical evidence hidden in the scientific literature, useful to validate approaches in the drug discovery process.

DDREL: From drug-drug relationships to drug repurposing

Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs’ anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups.

Repurposing – second life for drugs

Drug repurposing refers to finding new indications for existing drugs. The paradigm shift from traditional drug discovery to drug repurposing is driven by the fact that new drug pipelines are getting dried up because of mounting Research & Development (R&D) costs, long timeline for new drug development, low success rate for new molecular entities, regulatory hurdles coupled with revenue loss from patent expiry and competition from generics. Anaemic drug pipelines along with increasing demand for newer effective, cheaper, safer drugs and unmet medical needs call for new strategies of drug discovery and, drug repurposing seems to be a promising avenue for such endeavours. Drug repurposing strategies have progressed over years from simple serendipitous observations to more complex computational methods in parallel with our ever-growing knowledge on drugs, diseases, protein targets and signalling pathways but still the knowledge is far from complete. Repurposed drugs too have to face many obstacles, although lesser than new drugs, before being successful.

A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature

A biological pathway or regulatory network is a collection of molecular regulators which can activate the changes in cellular processes leading to an assembly of new molecules by series of actions among the molecules. There are three important pathways in system biology studies namely signaling pathways, metabolic pathways, and genetic pathways (or) gene regulatory networks. Recently, biological pathway construction from scientific literature is given much attention as the scientific literature contains a rich set of linguistic features to extract biological associations between genes and proteins. These associations can be united to construct biological networks. Here, we present a brief overview about various biological pathways, biomedical text resources/corpora for network construction and state-of-the-art existing methods for network construction followed by our hybrid text mining protocol for extracting pathways and regulatory networks from biomedical literature.

System and network biology-based computational approaches for drug repositioning

Recent advances in computational biology have not only fastened the drug discovery process but have also proven to be a powerful tool for the search of existing molecules of therapeutic value for drug repurposing. The system biology-based drug repurposing approaches shorten the time and reduced the cost of the whole process when compared to de novo drug discovery. In the present pandemic situation, these computational approaches have emerged as a boon to tackle the COVID-19 associated morbidities and mortalities. In this chapter, we present the overview of system biology-based network system approaches which can be exploited for the drug repurposing of disease. Besides, we have included information on relevant repurposed drugs which are currently used for the treatment of COVID-19.

Evaluation of Colocasia esculenta Schott in anti-cancerous properties with proximity extension assays

Background

Colocasia esculenta Schott (called as Xiangshayu in Chinese) is an excellent local cultivar of the genus polymorpha in Jiangsu Province, China.

Objective

In the present study, we have performed a comparative study before and after dietary consumption with Colocasia esculenta Schott to evaluate its anti-cancerous properties.

Design

Forty-two healthy volunteers were recruited, and dietary consumption with 200 g of tap water cooked Colocasia esculenta Schott daily was conducted for 1 month. Plasma samples from the subjects before and after dietary consumption with Colocasia esculenta Schott were analyzed with proximity extension assays for the alteration of 92 proteins in relation with cancers, while blood samples were examined for physiological parameters with an automatic biochemical analyzer. Bioinformatic analyses were conducted using MalaCards and GEPIA.

Results

After taking dietary consumption with Colocasia esculenta Schott, circulating CYR61, ANXA1, and VIM protein levels in the subjects was found to be most significantly downregulated, while for ITGB5, EPHA2, and CEACAM1, it was upregulated. Alternation of these proteins was predicted to be associated with the development of tumors such as pancreatic adenocarcinoma and breast and prostate cancers.

Conclusion

The present study provides evidence that Colocasia esculenta Schott, as a healthy food, has anti-cancerous properties. Further investigation of phytochemistry in Colocasia esculenta Schott has been taken into our consideration.

Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing

Machine learning and data-driven approaches are currently being widely used in drug discovery and development due to their potential advantages in decision-making based on the data leveraged from existing sources. Applying these approaches to drug repurposing (DR) studies can identify new relationships between drug molecules, therapeutic targets and diseases that will eventually help in generating new insights for developing novel therapeutics. In the current study, a dataset of 1671 approved drugs is analyzed using a combined approach involving unsupervised Machine Learning (ML) techniques (Principal Component Analysis (PCA) followed by k-means clustering) and Structure-Activity Relationships (SAR) predictions for DR. PCA is applied on all the two dimensional (2D) molecular descriptors of the dataset and the first five Principal Components (PC) were subsequently used to cluster the drugs into nine well separated clusters using k-means algorithm. We further predicted the biological activities for the drug-dataset using the PASS (Predicted Activities Spectra of Substances) tool. These predicted activity values are analyzed systematically to identify repurposable drugs for various diseases. Clustering patterns obtained from k-means showed that every cluster contains subgroups of structurally similar drugs that may or may not have similar therapeutic indications. We hypothesized that such structurally similar but therapeutically different drugs can be repurposed for the native indications of other drugs of the same cluster based on their high predicted biological activities obtained from PASS analysis. In line with this, we identified 66 drugs from the nine clusters which are structurally similar but have different therapeutic uses and can therefore be repurposed for one or more native indications of other drugs of the same cluster. Some of these drugs not only share a common substructure but also bind to the same target and may have a similar mechanism of action, further supporting our hypothesis. Furthermore, based on the analysis of predicted biological activities, we identified 1423 drugs that can be repurposed for 366 new indications against several diseases. In this study, an integrated approach of unsupervised ML and SAR analysis have been used to identify new indications for approved drugs and the study provides novel insights into clustering patterns generated through descriptor level analysis of approved drugs.

A Birds-Eye (Re)View of Acid-Suppression Drugs, COVID-19, and the Highly Variable Literature

This Perspective examines a recent surge of information regarding the potential benefits of acid-suppression drugs in the context of COVID-19, with a particular eye on the great variability (and, thus, confusion) that has arisen across the reported findings, at least as regards the popular antacid famotidine. The degree of inconsistency and discordance reflects contradictory conclusions from independent, clinical-based studies that took roughly similar approaches, in terms of both experimental design (retrospective, observational, cohort-based, etc.) and statistical analysis workflows (propensity-score matching and stratification into sub-cohorts, etc.). The contradictions and potential confusion have ramifications for clinicians faced with choosing therapeutically optimal courses of intervention: e.g., do any potential benefits of famotidine suggest its use in a particular COVID-19 case? (If so, what administration route, dosage regimen, duration, etc. are likely optimal?) As succinctly put this March in Freedberg et al. (2021), "…several retrospective studies show relationships between famotidine and outcomes in COVID-19 and several do not." Beyond the pressing issue of possible therapeutic indications, the conflicting data and conclusions related to famotidine must be resolved before its inclusion/integration in ontological and knowledge graph (KG)-based frameworks, which in turn are useful for drug discovery and repurposing. As a broader methodological issue, note that reconciling inconsistencies would bolster the validity of meta-analyses which draw upon the relevant data-sources. And, perhaps most broadly, developing a system for treating inconsistencies would stand to improve the qualities of both 1) real world evidence-based studies (retrospective), on the one hand, and 2) placebo-controlled, randomized multi-center clinical trials (prospective), on the other hand. In other words, a systematic approach to reconciling the two types of studies would inherently improve the quality and utility of each type of study individually.

A Novel Drug Repositioning Approach Based on Integrative Multiple Similarity Measures

BACKGROUND

Drug repositioning refers to discovering new indications for the existing drugs, which can improve the efficiency of drug research and development.

METHODS

In this work, a novel drug repositioning approach based on integrative multiple similarity measure, called DR_IMSM, is proposed. The process of integrative similarity measure contains three steps. First, a heterogeneous network can be constructed based on known drug-disease association, shared entities information for drug pairwise and diseases pairwise. Second, a deep learning method, DeepWalk, is used to capture the topology similarity for drug and disease. Third, a similarity integration and adjusting process is further conducted to obtain more comprehensive drug and disease similarity measure, respectively.

RESULTS

On this basis, a Bi-random walk algorithm is implemented in the constructed heterogeneous network to rank diseases for each drug. Compared with other approaches, the proposed DR_IMSM can achieve superior performance in terms of AUC on the gold standard datasets. Case studies further confirm the practical significance of DR_IMSM.

Representing a Heterogeneous Pharmaceutical Knowledge-Graph with Textual Information

We deal with a heterogeneous pharmaceutical knowledge-graph containing textual information built from several databases. The knowledge graph is a heterogeneous graph that includes a wide variety of concepts and attributes, some of which are provided in the form of textual pieces of information which have not been targeted in the conventional graph completion tasks. To investigate the utility of textual information for knowledge graph completion, we generate embeddings from textual descriptions given to heterogeneous items, such as drugs and proteins, while learning knowledge graph embeddings. We evaluate the obtained graph embeddings on the link prediction task for knowledge graph completion, which can be used for drug discovery and repurposing. We also compare the results with existing methods and discuss the utility of the textual information.

Perspectives on Drug Repurposing

Complex common diseases are a significant burden for our societies and demand not only preventive measures but also more effective, safer, and more affordable treatments. The whole process of the current model of drug discovery and development implies a high investment by the pharmaceutical industry, which ultimately impact in high drug prices. In this sense, drug repurposing would help meet the needs of patients to access useful and novel treatments. Unlike the traditional approach, drug repurposing enters both the preclinical evaluation and clinical trials of the compound of interest faster, budgeting research and development costs, and limiting potential biosafety risks. The participation of government, society, and private investors is needed to secure the funds for experimental design and clinical development of repurposing candidates to have affordable, effective, and safe repurposed drugs. Moreover, extensive advertising of repurposing as a concept in the health community, could reduce prescribing bias when enough clinical evidence exists, which will support the employment of cheaper and more accessible repurposed compounds for common conditions.

Recent advances in biomedical literature mining

The recent years have witnessed a rapid increase in the number of scientific articles in biomedical domain. These literature are mostly available and readily accessible in electronic format. The domain knowledge hidden in them is critical for biomedical research and applications, which makes biomedical literature mining (BLM) techniques highly demanding. Numerous efforts have been made on this topic from both biomedical informatics (BMI) and computer science (CS) communities. The BMI community focuses more on the concrete application problems and thus prefer more interpretable and descriptive methods, while the CS community chases more on superior performance and generalization ability, thus more sophisticated and universal models are developed. The goal of this paper is to provide a review of the recent advances in BLM from both communities and inspire new research directions.

IBDDB: a manually curated and text-mining-enhanced database of genes involved in inflammatory bowel disease

To date, research on inflammatory bowel disease (IBD, encompassing Crohn's disease and ulcerative colitis), a chronic complex disorder, has generated a large amount of data scattered across published literature (1 06 333) listed in PubMed on 14 October 2020, and no dedicated database currently exists that catalogues information on genes associated with IBD. We aimed to manually curate 289 genes that are experimentally validated to be linked with IBD and its known phenotypes. Furthermore, we have developed an integrated platform providing information about different aspects of these genes by incorporating several resources and an extensive text-mined knowledgebase. The curated IBD database (IBDDB) allows the selective display of collated 34 subject-specific concepts (listed as columns) exportable through a user-friendly IBDDB portal. The information embedded in concepts was acquired via text-mining of PubMed (manually cleaned and curated), accompanied by data-mining from varied resources. The user can also explore different biomedical entities and their co-occurrence with other entities (about one million) from 11 curated dictionaries in the indexed PubMed records. This functionality permits the user to generate and cross-examine a new hypothesis that is otherwise not easy to comprehend by just reading the published abstracts and papers. Users can download required information using various file formats and can display information in the form of networks. To our knowledge, no curated database of IBD-related genes is available so far. IBDDB is free for academic users and can be accessed at https://www.cbrc.kaust.edu.sa/ibd/.

An Analytical Review of Computational Drug Repurposing

Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in recent years in both the pharmaceutical industry and research community. It refers to the process of discovering new uses and indications for existing or failed drugs. It is cost-effective and reliable in contrast to experimental drug discovery, which is a costly, time-consuming, and risky process and limited to a relatively small number of targets. Accordingly, a plethora of computational methodologies have been propounded to repurpose drugs on a large scale by utilizing available high throughput data. The available literature, however, lacks a contemporary and comprehensive analysis of the current computational drug repurposing methodologies. In this paper, we presented a systematic analysis of computational drug repurposing which consists of three main sections: Initially, we categorize the computational drug repurposing methods based on their technical approach and artificial intelligence perspective and discuss the strengths and weaknesses of various methods. Secondly, some general criteria are recommended to analyze our proposed categorization. In the third and final section, a qualitative comparison is made between each approach which is a guide to understanding their preference to one another. Further, this systematic analysis can help in the efficient selection and improvement of drug repurposing techniques based on the nature of computational methods implemented on biological resources.

Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery

Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product. The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, assessing their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.

Web-based Tools for Drug Repurposing: Successful Examples of Collaborative Research

Computational approaches have been proven to be complementary tools of interest in identifying potential candidates for drug repurposing. However, although the methods developed so far offer interesting opportunities and could contribute to solving issues faced by the pharmaceutical sector, they also come with their constraints. Indeed, specific challenges ranging from data access, standardization and integration to the implementation of reliable and coherent validation methods must be addressed to allow systematic use at a larger scale. In this mini-review, we cover computational tools recently developed for addressing some of these challenges. This includes specific databases providing accessibility to a large set of curated data with standardized annotations, web-based tools integrating flexible user interfaces to perform fast computational repurposing experiments and standardized datasets specifically annotated and balanced for validating new computational drug repurposing methods. Interestingly, these new databases combined with the increasing number of information about the outcomes of drug repurposing studies can be used to perform a meta-analysis to identify key properties associated with successful drug repurposing cases. This information could further be used to design estimation methods to compute a priori assessment of the repurposing possibilities.

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors-let us call that the silver bullet approach to cancer therapeutics-to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns-we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases "shrapnel gunshots" may become more effective than "silver bullets". Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.

Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach

Background

In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets.

Results

We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting.

Conclusions

dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.

A Review of Recent Developments and Progress in Computational Drug Repositioning

Computational drug repositioning is an efficient approach towards discovering new indications for existing drugs. In recent years, with the accumulation of online health-related information and the extensive use of biomedical databases, computational drug repositioning approaches have achieved significant progress in drug discovery. In this review, we summarize recent advancements in drug repositioning. Firstly, we explicitly demonstrated the available data source information which is conducive to identifying novel indications. Furthermore, we provide a summary of the commonly used computing approaches. For each method, we briefly described techniques, case studies, and evaluation criteria. Finally, we discuss the limitations of the existing computing approaches.

Drug Repurposing: Considerations to Surpass While Re-directing Old Compounds for New Treatments

Drug repurposing has increased in recent years as an attractive option for treating a number of diseases. Compared to those brought forward via traditional chemical development, drugs intended for repurposing can enter the market faster and with lower investment from pharmaceutical companies. However, a common trend is to focus on diseases that yield higher returns to the industry, such as cancer and common metabolic and inflammatory conditions, resulting in orphan illnesses and neglected tropical diseases having fewer repurposing options for affected patients. In addition, certain legal concerns, including limited patent coverage for the repurposed drugs and pharmacological challenges in performing clinical trials, reduce the likelihood of success. In this review, we discuss the most important concerns that affect the pathway of drug repurposing, with special emphasis on the economic revenues, government-industry associations, and legal considerations that together impact the pharmaceutical industry's decision-making on which compounds may be eligible for repurposing.

Drug Repositioning: New Approaches and Future Prospects for Life-Debilitating Diseases and the COVID-19 Pandemic Outbreak

Traditionally, drug discovery utilises a de novo design approach, which requires high cost and many years of drug development before it reaches the market. Novel drug development does not always account for orphan diseases, which have low demand and hence low-profit margins for drug developers. Recently, drug repositioning has gained recognition as an alternative approach that explores new avenues for pre-existing commercially approved or rejected drugs to treat diseases aside from the intended ones. Drug repositioning results in lower overall developmental expenses and risk assessments, as the efficacy and safety of the original drug have already been well accessed and approved by regulatory authorities. The greatest advantage of drug repositioning is that it breathes new life into the novel, rare, orphan, and resistant diseases, such as Cushing's syndrome, HIV infection, and pandemic outbreaks such as COVID-19. Repositioning existing drugs such as Hydroxychloroquine, Remdesivir, Ivermectin and Baricitinib shows good potential for COVID-19 treatment. This can crucially aid in resolving outbreaks in urgent times of need. This review discusses the past success in drug repositioning, the current technological advancement in the field, drug repositioning for personalised medicine and the ongoing research on newly emerging drugs under consideration for the COVID-19 treatment.

Harnessing Unconventional T Cells for Immunotherapy of Tuberculosis

Even if the incidence of tuberculosis (TB) has been decreasing over the last years, the number of patients with TB is increasing worldwide. The emergence of multidrug-resistant and extensively drug-resistant TB is making control of TB more difficult. Mycobacterium bovis bacillus Calmette–Guérin vaccine fails to prevent pulmonary TB in adults, and there is an urgent need for a vaccine that is also effective in patients with human immunodeficiency virus (HIV) coinfection. Therefore, TB control may benefit on novel therapeutic options beyond antimicrobial treatment. Host-directed immunotherapies could offer therapeutic strategies for patients with drug-resistant TB or with HIV and TB coinfection. In the last years, the use of donor lymphocytes after hematopoietic stem cell transplantation has emerged as a new strategy in the cure of hematologic malignancies in order to induce graft-versus leukemia and graft-versus-infection effects. Moreover, adoptive therapy has proven to be effective in controlling cytomegalovirus and Epstein-Barr virus reactivation in immunocompromised patients with ex vivo expanded viral antigen-specific T cells. Unconventional T cells are a heterogeneous group of T lymphocytes with limited diversity. One of their characteristics is that antigen recognition is not restricted by the classical major histocompatibility complex (MHC). They include CD1 (cluster of differentiation 1)–restricted T cells, MHC-related protein-1–restricted mucosal-associated invariant T (MAIT) cells, MHC class Ib–reactive T cells, and γδ T cells. Because these T cells are genotype-independent, they are also termed “donor unrestricted” T cells. The combined features of low donor diversity and the lack of genetic restriction make these cells suitable candidates for T cell–based immunotherapy of TB.

The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation

BACKGROUND

Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug, we explored the landscape of the SARS-CoV-2 biomedical publications to identify potential treatments.

OBJECTIVE

The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials.

METHODS

To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called "diversity." A diversity score for a given drug was calculated by measuring how "diverse" a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease.

RESULTS

From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. The top 10 candidates according to our algorithm are hydroxychloroquine, azithromycin, chloroquine, ritonavir, losartan, remdesivir, favipiravir, methylprednisolone, rapamycin, and tilorone dihydrochloride.

CONCLUSIONS

The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.

ADDI: Recommending alternatives for drug-drug interactions with negative health effects

Investigating the interactions among various drugs is an indispensable issue in the field of computational biology. Scientific literature represents a rich source for the retrieval of knowledge about the interactions between drugs. Predicting drug-drug interaction (DDI) types will help biologists to evade hazardous drug interactions and support them in discovering potential alternatives that increase therapeutic efficacy and reduce toxicity. In this paper, we propose a general-purpose method called ADDI (standing for Alternative Drug-Drug Interaction) that applies deep learning on PubMed abstracts to predict interaction types among drugs. As an application, ADDI recommends alternatives for drug-drug interactions (DDIs) which have Negative Health Effects Types (NHETs). ADDI clearly outperforms state-of-the-art methods, on average by 13%, with respect to accuracy by using only the textual content of the online PubMed papers. Additionally, manual evaluation of ADDI indicates high precision in recommending alternatives for DDIs with NHETs.

Computational Drug Repositioning: Current Progress and Challenges

Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.

Knowledge-driven drug repurposing using a comprehensive drug knowledge graph

Due to the huge costs associated with new drug discovery and development, drug repurposing has become an important complement to the traditional de novo approach. With the increasing number of public databases and the rapid development of analytical methodologies, computational approaches have gained great momentum in the field of drug repurposing. In this study, we introduce an approach to knowledge-driven drug repurposing based on a comprehensive drug knowledge graph. We design and develop a drug knowledge graph by systematically integrating multiple drug knowledge bases. We describe path- and embedding-based data representation methods of transforming information in the drug knowledge graph into valuable inputs to allow machine learning models to predict drug repurposing candidates. The evaluation demonstrates that the knowledge-driven approach can produce high predictive results for known diabetes mellitus treatments by only using treatment information on other diseases. In addition, this approach supports exploratory investigation through the review of meta paths that connect drugs with diseases. This knowledge-driven approach is an effective drug repurposing strategy supporting large-scale prediction and the investigation of case studies.

Current trends in cancer immunotherapy: a literature-mining analysis

Cancer immunotherapy is a rapidly growing field that is completely transforming oncology care. Mining this knowledge base for biomedically important information is becoming increasingly challenging, due to the expanding number of scientific publications, and the dynamic evolution of this subject with time. In this study, we have employed a literature-mining approach that was used to analyze the cancer immunotherapy-related publications listed in PubMed and quantify emerging trends. A total of 93,033 publications published in 5055 journals have been retrieved, and 141 meaningful topics have been identified, which were further classified into eight distinct categories. Statistical analysis indicates a mean annual increase in the number of published papers of approximately 8% in the last 20 years. The research topics that exhibited the highest trends included "immune checkpoint inhibitors," "tumor microenvironment," "HPV vaccination," "CAR T-cells," and "gene mutations/tumor profiling." The top identified cancer types included "lung," "colorectal," and "breast cancer," and a shift in popularity from hematological to solid tumors was observed. As regards clinical research, a transition from early phase clinical trials to randomized control trials was recorded, indicating that the field is entering a more advanced phase of development. Overall, this mining approach provided an unbiased analysis of the cancer immunotherapy literature in a time-conserving and scale-efficient manner.

Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing

PURPOSE

Drug development is becoming increasingly expensive and time consuming. Drug repurposing is one potential solution to accelerate drug discovery. However, limited research exists on the use of electronic health record (EHR) data for drug repurposing, and most published studies have been conducted in a hypothesis-driven manner that requires a predefined hypothesis about drugs and new indications. Whether EHRs can be used to detect drug repurposing signals is not clear. We want to demonstrate the feasibility of mining large, longitudinal EHRs for drug repurposing by detecting candidate noncancer drugs that can potentially be used for the treatment of cancer.

PATIENTS AND METHODS

By linking cancer registry data to EHRs, we identified 43,310 patients with cancer treated at Vanderbilt University Medical Center (VUMC) and 98,366 treated at the Mayo Clinic. We assessed the effect of 146 noncancer drugs on cancer survival using VUMC EHR data and sought to replicate significant associations (false discovery rate < .1) using the identical approach with Mayo Clinic EHR data. To evaluate replicated signals further, we reviewed the biomedical literature and clinical trials on cancers for corroborating evidence.

RESULTS

We identified 22 drugs from six drug classes (statins, proton pump inhibitors, angiotensin-converting enzyme inhibitors, β-blockers, nonsteroidal anti-inflammatory drugs, and α-1 blockers) associated with improved overall cancer survival (false discovery rate < .1) from VUMC; nine of the 22 drug associations were replicated at the Mayo Clinic. Literature and cancer clinical trial evaluations also showed very strong evidence to support the repurposing signals from EHRs.

CONCLUSION

Mining of EHRs for drug exposure–mediated survival signals is feasible and identifies potential candidates for antineoplastic repurposing. This study sets up a new model of mining EHRs for drug repurposing signals.

Broad-coverage biomedical relation extraction with SemRep

BACKGROUND

In the era of information overload, natural language processing (NLP) techniques are increasingly needed to support advanced biomedical information management and discovery applications. In this paper, we present an in-depth description of SemRep, an NLP system that extracts semantic relations from PubMed abstracts using linguistic principles and UMLS domain knowledge. We also evaluate SemRep on two datasets. In one evaluation, we use a manually annotated test collection and perform a comprehensive error analysis. In another evaluation, we assess SemRep's performance on the CDR dataset, a standard benchmark corpus annotated with causal chemical-disease relationships.

RESULTS

A strict evaluation of SemRep on our manually annotated dataset yields 0.55 precision, 0.34 recall, and 0.42 F 1 score. A relaxed evaluation, which more accurately characterizes SemRep performance, yields 0.69 precision, 0.42 recall, and 0.52 F 1 score. An error analysis reveals named entity recognition/normalization as the largest source of errors (26.9%), followed by argument identification (14%) and trigger detection errors (12.5%). The evaluation on the CDR corpus yields 0.90 precision, 0.24 recall, and 0.38 F 1 score. The recall and the F 1 score increase to 0.35 and 0.50, respectively, when the evaluation on this corpus is limited to sentence-bound relationships, which represents a fairer evaluation, as SemRep operates at the sentence level.

CONCLUSIONS

SemRep is a broad-coverage, interpretable, strong baseline system for extracting semantic relations from biomedical text. It also underpins SemMedDB, a literature-scale knowledge graph based on semantic relations. Through SemMedDB, SemRep has had significant impact in the scientific community, supporting a variety of clinical and translational applications, including clinical decision making, medical diagnosis, drug repurposing, literature-based discovery and hypothesis generation, and contributing to improved health outcomes. In ongoing development, we are redesigning SemRep to increase its modularity and flexibility, and addressing weaknesses identified in the error analysis.