Automatic signal extraction, prioritizing and filtering approaches in detecting post-marketing cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS)

Identification of novel signal of Raynaud's phenomenon with Calcitonin Gene-Related Peptide(CGRP) antagonists using data mining algorithms and network pharmacological approaches

BACKGROUND

Calcitonin gene-related peptide (CGRP) antagonists are recently approved for the treatment of migraine.

AIM

The main aim of the current study was to find out the association of CGRP antagonists with RP using data mining algorithms integrated with network pharmacological approaches.

RESEARCH DESIGN AND METHODS

The individual case safety reports were extracted using OpenVigil2.1-MedDRA-V17 (2004Q1-2022Q3), the United States Adverse Event Reporting System (US FAERS). The data mining algorithms i.e. reporting odds ratio (ROR) with 95% confidence and proportionality reporting ratio (PRR) with associated chi-square value were calculated along with a minimum of three ICSRs to identify the signal. Further, the network was constructed using Cytoscape 3.7.2. Finally, molecular docking was performed using Glide, Schrodinger Inc.

RESULTS

The PRR ≥2 with a linked chi-square value ≥4, add up of co-occurrence ≥3, and a lower limit of 95% confidence interval of ROR exceeding 2 indicates a positive signal of RP. Further, the network pharmacological and molecular docking results have shown the involvement of insulin-like growth factor 1-receptor (IGF1R) pathways.

CONCLUSION

The RP is recognized as a novel signal with all CGRP antagonists.

Repurposing ketamine to treat cocaine use disorder: integration of artificial intelligence-based prediction, expert evaluation, clinical corroboration and mechanism of action analyses

BACKGROUND AND AIMS

Cocaine use disorder (CUD) is a significant public health issue for which there is no Food and Drug Administration (FDA) approved medication. Drug repurposing looks for new cost-effective uses of approved drugs. This study presents an integrated strategy to identify repurposed FDA-approved drugs for CUD treatment.

DESIGN

Our drug repurposing strategy combines artificial intelligence (AI)-based drug prediction, expert panel review, clinical corroboration and mechanisms of action analysis being implemented in the National Drug Abuse Treatment Clinical Trials Network (CTN). Based on AI-based prediction and expert knowledge, ketamine was ranked as the top candidate for clinical corroboration via electronic health record (EHR) evaluation of CUD patient cohorts prescribed ketamine for anesthesia or depression compared with matched controls who received non-ketamine anesthesia or antidepressants/midazolam. Genetic and pathway enrichment analyses were performed to understand ketamine's potential mechanisms of action in the context of CUD.

SETTING

The study utilized TriNetX to access EHRs from more than 90 million patients world-wide. Genetic- and functional-level analyses used DisGeNet, Search Tool for Interactions of Chemicals and Kyoto Encyclopedia of Genes and Genomes databases.

PARTICIPANTS

A total of 7742 CUD patients who received anesthesia (3871 ketamine-exposed and 3871 anesthetic-controlled) and 7910 CUD patients with depression (3955 ketamine-exposed and 3955 antidepressant-controlled) were identified after propensity score-matching.

MEASUREMENTS

EHR analysis outcome was a CUD remission diagnosis within 1 year of drug prescription.

FINDINGS

Patients with CUD prescribed ketamine for anesthesia displayed a significantly higher rate of CUD remission compared with matched individuals prescribed other anesthetics [hazard ratio (HR) = 1.98, 95% confidence interval (CI) = 1.42-2.78]. Similarly, CUD patients prescribed ketamine for depression evidenced a significantly higher CUD remission ratio compared with matched patients prescribed antidepressants or midazolam (HR = 4.39, 95% CI = 2.89-6.68). The mechanism of action analysis revealed that ketamine directly targets multiple CUD-associated genes (BDNF, CNR1, DRD2, GABRA2, GABRB3, GAD1, OPRK1, OPRM1, SLC6A3, SLC6A4) and pathways implicated in neuroactive ligand-receptor interaction, cAMP signaling and cocaine abuse/dependence.

CONCLUSIONS

Ketamine appears to be a potential repurposed drug for treatment of cocaine use disorder.

Integrin β3-Mediated Cell Senescence Associates with Gut Inflammation and Intestinal Degeneration in Models of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory loss and personality changes that ultimately lead to dementia. Currently, 50 million people worldwide suffer from dementia related to AD, and the pathogenesis underlying AD pathology and cognitive decline is unknown. While AD is primarily a neurological disease of the brain, individuals with AD often experience intestinal disorders, and gut abnormalities have been implicated as a major risk factor in the development of AD and relevant dementia. However, the mechanisms that mediate gut injury and contribute to the vicious cycle between gut abnormalities and brain injury in AD remain unknown. In the present study, a bioinformatics analysis was performed on the proteomics data of variously aged AD mouse colon tissues. We found that levels of integrin β3 and β-galactosidase (β-gal), two markers of cellular senescence, increased with age in the colonic tissue of mice with AD. The advanced artificial intelligence (AI)-based prediction of AD risk also demonstrated the association between integrin β3 and β-gal and AD phenotypes. Moreover, we showed that elevated integrin β3 levels were accompanied by senescence phenotypes and immune cell accumulation in AD mouse colonic tissue. Further, integrin β3 genetic downregulation abolished upregulated senescence markers and inflammatory responses in colonic epithelial cells in conditions associated with AD. We provide a new understanding of the molecular actions underpinning inflammatory responses during AD and suggest integrin β3 may function as novel target mediating gut abnormalities in this disease.

KG-Predict: A knowledge graph computational framework for drug repurposing

The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data has offered unprecedented opportunities for drug discovery including drug repurposing. Various knowledge graph-based methods have been developed to integrate and analyze complex and heterogeneous data sources to find new therapeutic applications for existing drugs. However, existing methods have limitations in modeling and capturing context-sensitive inter-relationships among tens of thousands of biomedical entities. In this paper, we developed KG-Predict: a knowledge graph computational framework for drug repurposing. We first integrated multiple types of entities and relations from various genotypic and phenotypic databases to construct a knowledge graph termed GP-KG. GP-KG was composed of 1,246,726 associations between 61,146 entities. KG-Predict then aggregated the heterogeneous topological and semantic information from GP-KG to learn low-dimensional representations of entities and relations, and further utilized these representations to infer new drug-disease interactions. In cross-validation experiments, KG-Predict achieved high performances [AUROC (the area under receiver operating characteristic) = 0.981, AUPR (the area under precision-recall) = 0.409 and MRR (the mean reciprocal rank) = 0.261], outperforming other state-of-art graph embedding methods. We applied KG-Predict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD) and showed that KG-Predict prioritized both FDA-approved and active clinical trial anti-AD drugs among the top (AUROC = 0.868 and AUPR = 0.364).

Target Adverse Event Profiles for Predictive Safety in the Postmarket Setting

We improved a previous pharmacological target adverse-event (TAE) profile model to predict adverse events (AEs) on US Food and Drug Administration (FDA) drug labels at the time of approval. The new model uses more drugs and features for learning as well as a new algorithm. Comparator drugs sharing similar target activities to a drug of interest were evaluated by aggregating AEs from the FDA Adverse Event Reporting System (FAERS), FDA drug labels, and medical literature. An ensemble machine learning model was used to evaluate FAERS case count, disproportionality scores, percent of comparator drug labels with a specific AE, and percent of comparator drugs with the reports of the event in the literature. Overall classifier performance was F1 of 0.71, area under the precision-recall curve of 0.78, and area under the receiver operating characteristic curve of 0.87. TAE analysis continues to show promise as a method to predict adverse events at the time of approval.

Combining phenome-driven drug-target interaction prediction with patients' electronic health records-based clinical corroboration toward drug discovery

Motivation

Predicting drug–target interactions (DTIs) using human phenotypic data have the potential in eliminating the translational gap between animal experiments and clinical outcomes in humans. One challenge in human phenome-driven DTI predictions is integrating and modeling diverse drug and disease phenotypic relationships. Leveraging large amounts of clinical observed phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients, we developed a novel integrated computational drug discovery approach by seamlessly combining DTI prediction and clinical corroboration.

Results

We developed a network-based DTI prediction system (TargetPredict) by modeling 855 904 phenotypic and genetic relationships among 1430 drugs, 4251 side effects, 1059 diseases and 17 860 genes. We systematically evaluated TargetPredict in de novo cross-validation and compared it to a state-of-the-art phenome-driven DTI prediction approach. We applied TargetPredict in identifying novel repositioned candidate drugs for Alzheimer’s disease (AD), a disease affecting over 5.8 million people in the United States. We evaluated the clinical efficiency of top repositioned drug candidates using EHRs of over 72 million patients. The area under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when evaluated using 910 drugs. TargetPredict outperformed a state-of-the-art phenome-driven DTI prediction system as measured by precision–recall curves [measured by average precision (MAP): 0.28 versus 0.23, P-value < 0.0001]. The EHR-based case–control studies identified that the prescriptions top-ranked repositioned drugs are significantly associated with lower odds of AD diagnosis. For example, we showed that the prescription of liraglutide, a type 2 diabetes drug, is significantly associated with decreased risk of AD diagnosis [adjusted odds ratios (AORs): 0.76; 95% confidence intervals (CI) (0.70, 0.82), P-value < 0.0001]. In summary, our integrated approach that seamlessly combines computational DTI prediction and large-scale patients’ EHRs-based clinical corroboration has high potential in rapidly identifying novel drug targets and drug candidates for complex diseases.

Availability and implementation

nlp.case.edu/public/data/TargetPredict.

Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study

Background

Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles.

Objective

The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration–approved immune checkpoint inhibitors.

Methods

In our framework, we first used the Food and Drug Administration’s Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals.

Results

By our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals.

Conclusions

We demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.

A Drug-Side Effect Context-Sensitive Network approach for drug target prediction

SUMMARY

Computational drug target prediction has become an important process in drug discovery. Network-based approaches are commonly used in computational drug-target interaction (DTI) prediction. Existing network-based approaches are limited in capturing the contextual information on how diseases, drugs and genes are connected. Here, we proposed a context-sensitive network (CSN) model for DTI prediction by modeling contextual drug phenotypic relationships. We constructed a Drug-Side Effect Context-Sensitive Network (DSE-CSN) of 139 760 drug-side effect pairs, representing 1480 drugs and 5868 side effects. We also built a protein-protein interaction network (PPIN) of 15 267 gene nodes and 178 972 weighted edges. A heterogeneous network was built by connecting the DSE-CSN and the PPIN through 3684 known DTIs. For each drug on the DSE-CSN, its genetic targets were predicted and prioritized using a network-based ranking algorithm. Our approach was evaluated in both de novo and leave-one-out cross-validation analysis using known DTIs as the gold standard. We compared our DSE-CSN-based model to the traditional similarity-based network (SBN)-based prediction model. The results suggested that the DSE-CSN-based model was able to rank known DTIs highly. In a de novo cross-validation, the area under the receiver operating characteristic (ROC) curve was 0.95. In a leave-one-out cross-validation, the average rank was top 3.2% for known DTIs. When it was compared to the SBN-based model using the Precision-Recall curve, our CSN-based model achieved a higher mean average precision (MAP) (0.23 versus 0.19, P-value<1e-4) in a de novo cross-validation analysis. We further improved the CSN-based DTI prediction by differentially weighting the drug-side effect pairs on the network and showed a significant improvement of the MAP (0.29 versus 0.23, P-value<1e-4). We also showed that the CSN-based model consistently achieved better performances than the traditional SBN-based model across different drug classes. Moreover, we demonstrated that our novel DTI predictions can be supported by published literature. In summary, the CSN-based model, by modeling the context-specific inter-relationships among drugs and side effects, has a high potential in drug target prediction.

AVAILABILITY AND IMPLEMENTATION

nlp/case/edu/public/data/DSE/CSN_DTI.

Predicting potential adverse events using safety data from marketed drugs

BACKGROUND

While clinical trials are considered the gold standard for detecting adverse events, often these trials are not sufficiently powered to detect difficult to observe adverse events. We developed a preliminary approach to predict 135 adverse events using post-market safety data from marketed drugs. Adverse event information available from FDA product labels and scientific literature for drugs that have the same activity at one or more of the same targets, structural and target similarities, and the duration of post market experience were used as features for a classifier algorithm. The proposed method was studied using 54 drugs and a probabilistic approach of performance evaluation using bootstrapping with 10,000 iterations.

RESULTS

Out of 135 adverse events, 53 had high probability of having high positive predictive value. Cross validation showed that 32% of the model-predicted safety label changes occurred within four to nine years of approval (median: six years).

CONCLUSIONS

This approach predicts 53 serious adverse events with high positive predictive values where well-characterized target-event relationships exist. Adverse events with well-defined target-event associations were better predicted compared to adverse events that may be idiosyncratic or related to secondary target effects that were poorly captured. Further enhancement of this model with additional features, such as target prediction and drug binding data, may increase accuracy.

Does Industry-Conducted All-Case Surveillance of Newly Approved Oncology Drugs Contribute to the Revision of Package Inserts in Japan?

In Japan, the Pharmaceuticals and Medical Devices Agency requires all-case surveillance studies (ACSS) for many novel oncology drugs as a condition for approval. However, this is a major burden on the pharmaceutical industry and clinicians. The objective of this analysis was to investigate whether ACSS can contribute essential new information on severe adverse drug reactions, which are necessary to revise the package inserts of drugs. All oncology drugs for which ACSS were required from January 2006-September 2015 found on the Pharmaceuticals and Medical Devices Agency website were reviewed, and the influence of ACSS on the package insert content was evaluated. Most of the package insert revisions regarding serious treatment-related adverse events were based on spontaneous reports from clinicians. The contribution of ACSS results to the revision of package inserts is limited and comes at the cost of financial resources and labor. An alternative, more efficient adverse-event reporting system is necessary.

Auto-Generated Physiological Chain Data for an Ontological Framework for Pharmacology and Mechanism of Action to Determine Suspected Drugs in Cases of Dysuria

INTRODUCTION

Patients often take several different medications for multiple conditions concurrently. Therefore, when adverse drug events (ADEs) occur, it is necessary to consider the mechanisms responsible. Few approaches consider the mechanisms of ADEs, such as changes in physiological states. We proposed that the ontological framework for pharmacology and mechanism of action (pharmacodynamics) we developed could be used for this approach. However, the existing knowledge base contains little data on physiological chains (PCs).

OBJECTIVE

We aimed to investigate a method for automatically generating missing PC from the viewpoint of anatomical structures. This study was conducted to determine dysuria-related adverse events more likely to occur during multidrug administration.

METHODS

We adopted a systematic approach to determine drugs suspected to cause adverse events and incorporated existing data and data generated in our newly developed method into our ontological framework. The performance of automated data generation was evaluated using this newly developed system. Suspected drugs determined by the system were compared with those derived from adverse events databases.

RESULTS

Of the 242 drugs involving suspected drug-induced urinary retention or dysuria, 26 suspected drugs were determined. Of these, five were drugs with side effects not listed in drug package inserts. The system derived potential mechanisms of action, PCs, and suspected drugs.

CONCLUSION

Our method is novel in that it generates PC data from anatomical structural properties and could serve as a knowledge base for determining suspected drugs by potential mechanisms of action.

Detecting Signals of Dietary Supplement Adverse Events from the CFSAN Adverse Event Reporting System (CAERS)

Dietary supplement adverse events are potentially severe, yet knowledge regarding the safety of dietary supplements is limited. The CFSAN Adverse Event Reporting System (CAERS) contains records of adverse events attributed to supplements and is potentially useful for dietary supplement pharmacovigilance. This study investigates the feasibility of mining CAERS for dietary supplement adverse events as well as for monitoring the safety of dietary supplement products. Using three online resources, we mapped products in CAERS to their listed ingredients. We then ran four standard signal detection algorithms over the ingredient-adverse event and product-adverse event pairs extracted from CAERS and ranked the detected associations. Comparing 130 signals detected by all four algorithms with a dietary supplement resource, we found evidence for 73 (56%) associations. In addition, some detected product-adverse event signals were consistent with product safety information. We have made a database of the detected adverse events publicly available at https://github.com/zhang-informatics/DDSAE.

Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data

Background

Systems approaches in studying disease relationship have wide applications in biomedical discovery, such as disease mechanism understanding and drug discovery. The FDA Adverse Event Reporting System (FAERS) contains rich information about patient diseases, medications, drug adverse events and demographics of 17 million case reports. Here, we explored this data resource to mine disease comorbidity relationships using association rule mining algorithm and constructed a disease comorbidity network.

Results

We constructed a disease comorbidity network with 1059 disease nodes and 12,608 edges using association rule mining of FAERS (14,157 rules). We evaluated the performance of comorbidity mining from FAERS using known disease comorbidities of multiple sclerosis (MS), psoriasis and obesity that represent rare, moderate and common disease respectively. Comorbidities of MS, obesity and psoriasis obtained from our network achieved precisions of 58.6%, 73.7%, 56.2% and recalls 87.5%, 69.2% and 72.7% separately. We performed comparative analysis of the disease comorbidity network with disease semantic network, disease genetic network and disease treatment network. We showed that (1) disease comorbidity clusters exhibit significantly higher semantic similarity than random network (0.18 vs 0.10); (2) disease comorbidity clusters share significantly more genes (0.46 vs 0.06); and (3) disease comorbidity clusters share significantly more drugs (0.64 vs 0.17). Finally, we demonstrated that the disease comorbidity network has potential in uncovering novel disease relationships using asthma as a case study.

Conclusions

Our study presented the first comprehensive attempt to build a disease comorbidity network from FDA Adverse Event Reporting System. This network shows well correlated with disease semantic similarity, disease genetics and disease treatment, which has great potential in disease genetics prediction and drug discovery.

Disease comorbidity-guided drug repositioning: a case study in schizophrenia

The key to any computational drug repositioning is the availability of relevant data in machine-understandable format. While large amount of genetic, genomic and chemical data are publicly available, large-scale higher-level disease and drug phenotypic data are limited. We recently constructed a large-scale disease-comorbidity relationship knowledge base (dCombKB) and a comprehensive drug-treatment relationship knowledge base (TreatKB) from 21 million biomedical research articles and other resources. In this study, we demonstrated the potential of dCombKB and TreatKB in drug repositioning for schizophrenia, one of the top ten illnesses contributing to the global burden of disease. dCombKB contains 121,359 unique disease-disease comorbidity pairs for 23,041 diseases. TreatKB contains 208,330 unique drug-disease treatment pairs for 2,484 drugs and 24,511 diseases. We constructed a phenotypic comorbidity disease network (PDN) of 14,645 disease nodes and 101,275 edges based on dCombKB. We applied standard network-based ranking algorithm to find diseases that are phenotypically related to SCZ. We developed a drug prioritization system, PhenoPredict-CDN, to systematically reposition drugs for SCZ from diseases phenotypically related to SCZ. PhenoPredict-CDN found all 18 FDA-approved SCZ drugs and ranked them highly as tested in a de-novo validation setting (recall: 1.0, mean ranking: top 6.05%, median ranking: top 1.65%). When compared to PREDICT, a comprehensive drug repositioning system, for novel predictions, Pheno-Predict-CDN outperformed PREDICT in Precision-Recall (PR) curves across three different evaluation datasets. Compared to PREDICT, PhenoPredict-CDN showed a significant 110.0-230.0% improvements in mean average precision. In summary, large-scale higher-level disease-comorbidity relationships data extracted from biomedical literature has potential in drug discovery for SCZ, a complex disease with unknown pathophysiological mechanisms. All the data are publicly available: dCombKB at http://nlp.

CASE

edu/public/data/dCombKB, TreatKB at http://nlp.

CASE

edu/public/data/treatKB/, and predictions for SCZ at http://nlp.

CASE

edu/public/data/SCZ_CDN/.

Target-Adverse Event Profiles to Augment Pharmacovigilance: A Pilot Study With Six New Molecular Entities

Clinical trials can fail to detect rare adverse events (AEs). We assessed the ability of pharmacological target adverse‐event (TAE) profiles to predict AEs on US Food and Drug Administration (FDA) drug labels at least 4 years after approval. TAE profiles were generated by aggregating AEs from the FDA adverse event reporting system (FAERS) reports and the FDA drug labels for drugs that hit a common target. A genetic algorithm (GA) was used to choose the adverse event (AE) case count (N), disproportionality score in FAERS (proportional reporting ratio (PRR)), and percent of comparator drug labels with an AE to maximize F‐measure. With FAERS data alone, precision, recall, and specificity were 0.57, 0.78, and 0.61, respectively. After including FDA drug label data, precision, recall, and specificity improved to 0.67, 0.81, and 0.71, respectively. Eighteen of 23 (78%) postmarket label changes were identified correctly. TAE analysis shows promise as a method to predict AEs at the time of drug approval.

Immunotherapy-related adverse events (irAEs): extraction from FDA drug labels and comparative analysis

Objectives

Immune checkpoint inhibitors (ICIs) have dramatically improved outcomes in cancer patients. However, ICIs are associated with significant immune-related adverse events (irAEs) and the underlying biological mechanisms are not well-understood. To ensure safe cancer treatment, research efforts are needed to comprehensively detect and understand irAEs.

Materials and methods

We manually extracted and standardized irAEs from The U.S Food and Drug Administration (FDA) drug labels for six FDA-approved ICIs. We compared irAE profile similarities among ICIs and 1507 FDA-approved non-ICI drugs. We investigated how irAEs have differential effects on human organs by classifying irAEs based on their targeted organ systems. Finally, we identified broad-spectrum (nontarget-specific) and narrow-spectrum (target-specific) irAEs.

Results

A total of 893 irAEs were extracted. 31.4% irAEs were shared among ICIs as compared to the 8.0% between ICIs and non-ICI drugs. irAEs were resulted from both on- and off-target effects: irAE profiles were more similar for ICIs with same target than different targets, demonstrating the on-target effects; irAE profile similarity for ICIs with the same target is less than 50%, demonstrating unknown off-target effects. ICIs significantly target many organ systems, including endocrine system (3.4-fold enrichment), metabolism (3.7-fold enrichment), immune system (3.6-fold enrichment), and autoimmune system (4.8-fold enrichment). We identified 21 broad-spectrum irAEs shared among all ICIs, 20 irAEs specific for PD-L1/PD-1 inhibition, and 28 irAEs specific for CTLA-4 inhibition.

Discussion and conclusion

Our study presents the first effort toward building a standardized database of irAEs. The extracted irAEs can serve as the gold standard for automatic irAE extractions from other data resources and set a foundation to understand biological mechanisms of irAEs.

Spontaneous Reporting on Adverse Events by Consumers in the United States: An Analysis of the Food and Drug Administration Adverse Event Reporting System Database

BACKGROUND

Voluntary reports on adverse events (AEs) submitted by consumers have been facilitated through the MedWatch program in the United States (US), but few studies have described the characteristics of voluntary reports.

OBJECTIVE

The aim of this study was to reveal the characteristics of current voluntary reports on AEs reported by consumers and healthcare professionals.

METHODS

We performed analysis on voluntary reports of AEs in the US Food and Drug Administration AE Reporting System (FAERS) database submitted in 2016. We compared reports by consumers with those by healthcare professionals.

RESULTS

The number of voluntary reports by consumers has increased since 2013 in the US. Reports by consumers were different from ones by health professionals in several important aspects such as demographics and outcomes of patients, AEs, and suspect drugs. The proportion of reports on female patients and on disability as a patient outcome were higher in reports by consumers than in those by healthcare professionals. Consumers more frequently reported concomitant drugs compared with healthcare professionals. Time to report varied among the occupations and depending on seriousness of outcomes.

CONCLUSIONS

Our analysis of voluntary AE reports in the US FAERS database has shown that voluntary reports tended to include AEs related to subjective symptoms, as in some previous studies on patient reporting in the EU. Voluntary reports by consumers seemed to be different from ones by healthcare professionals in important aspects including demographics and reporting behaviors. These findings suggest that the heterogeneities should be addressed appropriately in using spontaneous reports.

Drug repositioning for prostate cancer: using a data-driven approach to gain new insights

Prostate cancer (PC) is the most common cancer and the third leading cause of cancer death in men worldwide. Despite its high incidence and mortality, the likelihood of a cure is low for late-stages of PC. There is an unmet need for more effective agents for treating PC. Here, we present a drug repositioning system, GenoPredict, for finding innovative drug candidates for treating PC. GenoPredict leverages upon a large amount of disease genomics data and a large-scale drug treatment knowledge base (TreatKB) that we recently constructed. We first constructed a genetic disease network (GDN) that comprised of 882 nodes and 200,758 edges and applied a network-based ranking algorithm to find diseases from GDN that are genetically related to PC. We developed a drug prioritization algorithm to reposition drugs from PC-related diseases to treat PC. When evaluated in a de-novo prediction setting using 27 FDA- approved PC drugs, GenoPredict found 25 of 27 FDA-approved PC drugs and ranked them highly (recall: 0.925, mean ranking: 27.3%, median ranking: 15.6%). When compared to PREDICT, a comprehensive drug repositioning system, in novel predictions, GenoPredict performed better than PREDICT across two evaluation datasets. GenoPredict achieved a mean average precision (MAP) of 0.447 when evaluated with 172 PC drugs extracted from 172,888 clinical trial reports, representing a 164.5% improvement as compared to a MAP of 0.169 for PREDICT. When evaluated with 72 PC drugs extracted from 43,811 ongoing clinical trial reports, GenoPredict achieved a MAP of 0.278, representing a 231.1% improvement as compared to a MAP of 0.084 for PREDICT. The data is publicly available at: http://nlp.

CASE

edu/public/data/PC_GenoPredict and http: //nlp.

CASE

edu/public/data/treatKB.

Translating New Science Into the Drug Review Process: The US FDA's Division of Applied Regulatory Science

In 2011, the US Food and drug Administration (FDA) developed a strategic plan for regulatory science that focuses on developing new tools, standards, and approaches to assess the safety, efficacy, quality, and performance of FDA-regulated products. In line with this, the Division of Applied Regulatory Science was created to move new science into the Center for Drug Evaluation and Research (CDER) review process and close the gap between scientific innovation and drug review. The Division, located in the Office of Clinical Pharmacology, is unique in that it performs mission-critical applied research and review across the translational research spectrum including in vitro and in vivo laboratory research, in silico computational modeling and informatics, and integrated clinical research covering clinical pharmacology, experimental medicine, and postmarket analyses. The Division collaborates with Offices throughout CDER, across the FDA, other government agencies, academia, and industry. The Division is able to rapidly form interdisciplinary teams of pharmacologists, biologists, chemists, computational scientists, and clinicians to respond to challenging regulatory questions for specific review issues and for longer-range projects requiring the development of predictive models, tools, and biomarkers to speed the development and regulatory evaluation of safe and effective drugs. This article reviews the Division's recent work and future directions, highlighting development and validation of biomarkers; novel humanized animal models; translational predictive safety combining in vitro, in silico, and in vivo clinical biomarkers; chemical and biomedical informatics tools for safety predictions; novel approaches to speed the development of complex generic drugs, biosimilars, and antibiotics; and precision medicine.

Drug repurposing for glioblastoma based on molecular subtypes

A recent multi-platform analysis by The Cancer Genome Atlas identified four distinct molecular subtypes for glioblastoma (GBM) and demonstrated that the subtypes correlate with clinical phenotypes and treatment responses. In this study, we developed a computational drug repurposing approach to predict GBM drugs based on the molecular subtypes. Our approach leverages the genomic signature for each GBM subtype, and integrates the human cancer genomics with mouse phenotype data to identify the opportunity of reusing the FDA-approved agents to treat specific GBM subtypes. Specifically, we first constructed the phenotype profile for each GBM subtype using their genomic signatures. For each approved drug, we also constructed a phenotype profile using the drug target genes. Then we developed an algorithm to match and prioritize drugs based on their phenotypic similarities to the GBM subtypes. Our approach is highly generalizable for other disorders if provided with a list of disorder-specific genes. We first evaluated the approach in predicting drugs for the whole GBM. For a combined set of approved, potential and off-label GBM drugs, we achieved a median rank of 9.3%, which is significantly higher (p Collapse

Key Words

• Drug discovery
• Glioblastoma
• Molecular subtypes
• Precision medicine

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MESH Headings

• Algorithms
• Animals
• Brain Neoplasms/drug therapy
• Brain Neoplasms/genetics
• Drug Repositioning
• Genomics
• Glioblastoma/drug therapy
• Glioblastoma/genetics
• Humans
• Mice
• Phenotype

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Grants

• DP2 HD084068 NICHD NIH HHS
• UL1 TR000439 NCATS NIH HHS

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Affiliation(s)

Yang Chen Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, United States
Rong Xu Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, United States.

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A genomics-based systems approach towards drug repositioning for rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and destruction of synovial joints. RA affects up to 1 % of the population worldwide. Currently, there are no drugs that can cure RA or achieve sustained remission. The unknown cause of the disease represents a significant challenge in the drug development. In this study, we address this challenge by proposing an alternative drug discovery approach that integrates and reasons over genetic interrelationships between RA and other genetic diseases as well as a large amount of higher-level drug treatment data.

We first constructed a genetic disease network using disease genetics data from Genome-Wide Association Studies (GWAS). We developed a network-based ranking algorithm to prioritize diseases genetically-related to RA (RA-related diseases). We then developed a drug prioritization algorithm to reposition drugs from RA-related diseases to treat RA.

Results

Our algorithm found 74 of the 80 FDA-approved RA drugs and ranked them highly (recall: 0.925, median ranking: 8.93 %), demonstrating the validity of our strategy. When compared to a study that used GWAS data to directly connect RA-associated genes to drug targets (“direct genetics-based” approach), our algorithm (“indirect genetics-based”) achieved a comparable overall performance, but complementary precision and recall in retrospective validation (precision: 0.22, recall: 0.36; F1: 0.27 vs. precision: 0.74, recall: 0.16; F1: 0.28). Our approach performed significantly better in novel predictions when evaluated using 165 not-yet-FDA-approved RA drugs (precision: 0.46, recall: 0.50; F1: 0.47 vs. precision: 0.40, recall: 0.006; F1: 0.01).

Conclusions

In summary, although the fundamental pathophysiological mechanisms remain uncharacterized, our proposed computation-based drug discovery approach to analyzing genetic and treatment interrelationships among thousands of diseases and drugs can facilitate the discovery of innovative drugs for treating RA.

Explore Small Molecule-induced Genome-wide Transcriptional Profiles for Novel Inflammatory Bowel Disease Drug

Inflammatory Bowel Disease (IBD) is a chronic and relapsing disorder, which affects millions people worldwide. Current drug options cannot cure the disease and may cause severe side effects. We developed a systematic framework to identify novel IBD drugs exploiting millions of genomic signatures for chemical compounds. Specifically, we searched all FDA-approved drugs for candidates that share similar genomic profiles with IBD. In the evaluation experiments, our approach ranked approved IBD drugs averagely within top 26% among 858 candidates, significantly outperforming a state-of-art genomics-based drug repositioning method (p-value < e-8). Our approach also achieved significantly higher average precision than the state-of-art approach in predicting potential IBD drugs from clinical trials (0.072 vs. 0.043, p<0.1) and off-label IBD drugs (0.198 vs. 0.138, p<0.1). Furthermore, we found evidences supporting the therapeutic potential of the top-ranked drugs, such as Naloxone, in literature and through analyzing target genes and pathways.

Combining Human Disease Genetics and Mouse Model Phenotypes towards Drug Repositioning for Parkinson's disease

Parkinson's disease (PD) is a severe neurodegenerative disorder without effective treatments. Here, we present a novel drug repositioning approach to predict new drugs for PD leveraging both disease genetics and large amounts of mouse model phenotypes. First, we identified PD-specific mouse phenotypes using well-studied human disease genes. Then we searched all FDA-approved drugs for candidates that share similar mouse phenotype profiles with PD. We demonstrated the validity of our approach using drugs that have been approved for PD: 10 approved PD drugs were ranked within top 10% among 1197 candidates. In predicting novel PD drugs, our approach achieved a mean average precision of 0.24, which is significantly higher (p Collapse

Key Words Collapse

MESH Headings

• Animals
• Drug Discovery
• Drug Repositioning
• Gene Expression Profiling
• Humans
• Mice
• Parkinson Disease/drug therapy
• Parkinson Disease/genetics
• Phenotype

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Grants

• DP2 HD084068 NICHD NIH HHS

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Affiliation(s)

Yang Chen Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, Ohio, USA
Xiaoshu Cai Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, Ohio, USA
Rong Xu Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA

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DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue

Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

PhenoPredict: A disease phenome-wide drug repositioning approach towards schizophrenia drug discovery

Schizophrenia (SCZ) is a common complex disorder with poorly understood mechanisms and no effective drug treatments. Despite the high prevalence and vast unmet medical need represented by the disease, many drug companies have moved away from the development of drugs for SCZ. Therefore, alternative strategies are needed for the discovery of truly innovative drug treatments for SCZ. Here, we present a disease phenome-driven computational drug repositioning approach for SCZ. We developed a novel drug repositioning system, PhenoPredict, by inferring drug treatments for SCZ from diseases that are phenotypically related to SCZ. The key to PhenoPredict is the availability of a comprehensive drug treatment knowledge base that we recently constructed. PhenoPredict retrieved all 18 FDA-approved SCZ drugs and ranked them highly (recall=1.0, and average ranking of 8.49%). When compared to PREDICT, one of the most comprehensive drug repositioning systems currently available, in novel predictions, PhenoPredict represented clear improvements over PREDICT in Precision-Recall (PR) curves, with a significant 98.8% improvement in the area under curve (AUC) of the PR curves. In addition, we discovered many drug candidates with mechanisms of action fundamentally different from traditional antipsychotics, some of which had published literature evidence indicating their treatment benefits in SCZ patients. In summary, although the fundamental pathophysiological mechanisms of SCZ remain unknown, integrated systems approaches to studying phenotypic connections among diseases may facilitate the discovery of innovative SCZ drugs.

Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles

Targeted anticancer drugs such as imatinib, trastuzumab and erlotinib dramatically improved treatment outcomes in cancer patients, however, these innovative agents are often associated with unexpected side effects. The pathophysiological mechanisms underlying these side effects are not well understood. The availability of a comprehensive knowledge base of side effects associated with targeted anticancer drugs has the potential to illuminate complex pathways underlying toxicities induced by these innovative drugs. While side effect association knowledge for targeted drugs exists in multiple heterogeneous data sources, published full-text oncological articles represent an important source of pivotal, investigational, and even failed trials in a variety of patient populations. In this study, we present an automatic process to extract targeted anticancer drug-associated side effects (drug-SE pairs) from a large number of high profile full-text oncological articles. We downloaded 13,855 full-text articles from the Journal of Oncology (JCO) published between 1983 and 2013. We developed text classification, relationship extraction, signaling filtering, and signal prioritization algorithms to extract drug-SE pairs from downloaded articles. We extracted a total of 26,264 drug-SE pairs with an average precision of 0.405, a recall of 0.899, and an F1 score of 0.465. We show that side effect knowledge from JCO articles is largely complementary to that from the US Food and Drug Administration (FDA) drug labels. Through integrative correlation analysis, we show that targeted drug-associated side effects positively correlate with their gene targets and disease indications. In conclusion, this unique database that we built from a large number of high-profile oncological articles could facilitate the development of computational models to understand toxic effects associated with targeted anticancer drugs.

Comparing a knowledge-driven approach to a supervised machine learning approach in large-scale extraction of drug-side effect relationships from free-text biomedical literature

BACKGROUND

Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for both drug target discovery and drug repositioning. However, a comprehensive drug-SE association knowledge base does not exist. In this study, we present a novel knowledge-driven (KD) approach to effectively extract a large number of drug-SE pairs from published biomedical literature.

DATA AND METHODS

For the text corpus, we used 21,354,075 MEDLINE records (119,085,682 sentences). First, we used known drug-SE associations derived from FDA drug labels as prior knowledge to automatically find SE-related sentences and abstracts. We then extracted a total of 49,575 drug-SE pairs from MEDLINE sentences and 180,454 pairs from abstracts.

RESULTS

On average, the KD approach has achieved a precision of 0.335, a recall of 0.509, and an F1 of 0.392, which is significantly better than a SVM-based machine learning approach (precision: 0.135, recall: 0.900, F1: 0.233) with a 73.0% increase in F1 score. Through integrative analysis, we demonstrate that the higher-level phenotypic drug-SE relationships reflects lower-level genetic, genomic, and chemical drug mechanisms. In addition, we show that the extracted drug-SE pairs can be directly used in drug repositioning.

CONCLUSION

In summary, we automatically constructed a large-scale higher-level drug phenotype relationship knowledge, which can have great potential in computational drug discovery.

Combining automatic table classification and relationship extraction in extracting anticancer drug-side effect pairs from full-text articles

Anticancer drug-associated side effect knowledge often exists in multiple heterogeneous and complementary data sources. A comprehensive anticancer drug-side effect (drug-SE) relationship knowledge base is important for computation-based drug target discovery, drug toxicity predication and drug repositioning. In this study, we present a two-step approach by combining table classification and relationship extraction to extract drug-SE pairs from a large number of high-profile oncological full-text articles. The data consists of 31,255 tables downloaded from the Journal of Oncology (JCO). We first trained a statistical classifier to classify tables into SE-related and -unrelated categories. We then extracted drug-SE pairs from SE-related tables. We compared drug side effect knowledge extracted from JCO tables to that derived from FDA drug labels. Finally, we systematically analyzed relationships between anti-cancer drug-associated side effects and drug-associated gene targets, metabolism genes, and disease indications. The statistical table classifier is effective in classifying tables into SE-related and -unrelated (precision: 0.711; recall: 0.941; F1: 0.810). We extracted a total of 26,918 drug-SE pairs from SE-related tables with a precision of 0.605, a recall of 0.460, and a F1 of 0.520. Drug-SE pairs extracted from JCO tables is largely complementary to those derived from FDA drug labels; as many as 84.7% of the pairs extracted from JCO tables have not been included a side effect database constructed from FDA drug labels. Side effects associated with anticancer drugs positively correlate with drug target genes, drug metabolism genes, and disease indications.

Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature

Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for drug target discovery, drug repositioning, and drug toxicity prediction. However, currently available drug-SE association databases are far from being complete. Herein, in an effort to increase the data completeness of current drug-SE relationship resources, we present an automatic learning approach to accurately extract drug-SE pairs from the vast amount of published biomedical literature, a rich knowledge source of side effect information for commercial, experimental, and even failed drugs. For the text corpus, we used 119,085,682 MEDLINE sentences and their parse trees. We used known drug-SE associations derived from US Food and Drug Administration (FDA) drug labels as prior knowledge to find relevant sentences and parse trees. We extracted syntactic patterns associated with drug-SE pairs from the resulting set of parse trees. We developed pattern-ranking algorithms to prioritize drug-SE-specific patterns. We then selected a set of patterns with both high precisions and recalls in order to extract drug-SE pairs from the entire MEDLINE. In total, we extracted 38,871 drug-SE pairs from MEDLINE using the learned patterns, the majority of which have not been captured in FDA drug labels to date. On average, our knowledge-driven pattern-learning approach in extracting drug-SE pairs from MEDLINE has achieved a precision of 0.833, a recall of 0.407, and an F1 of 0.545. We compared our approach to a support vector machine (SVM)-based machine learning and a co-occurrence statistics-based approach. We show that the pattern-learning approach is largely complementary to the SVM- and co-occurrence-based approaches with significantly higher precision and F1 but lower recall. We demonstrated by correlation analysis that the extracted drug side effects correlate positively with both drug targets, metabolism, and indications.

Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection

BACKGROUND

Independent data sources can be used to augment post-marketing drug safety signal detection. The vast amount of publicly available biomedical literature contains rich side effect information for drugs at all clinical stages. In this study, we present a large-scale signal boosting approach that combines over 4 million records in the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and over 21 million biomedical articles.

RESULTS

The datasets are comprised of 4,285,097 records from FAERS and 21,354,075 MEDLINE articles. We first extracted all drug-side effect (SE) pairs from FAERS. Our study implemented a total of seven signal ranking algorithms. We then compared these different ranking algorithms before and after they were boosted with signals from MEDLINE sentences or abstracts. Finally, we manually curated all drug-cardiovascular (CV) pairs that appeared in both data sources and investigated whether our approach can detect many true signals that have not been included in FDA drug labels. We extracted a total of 2,787,797 drug-SE pairs from FAERS with a low initial precision of 0.025. The ranking algorithm combined signals from both FAERS and MEDLINE, significantly improving the precision from 0.025 to 0.371 for top-ranked pairs, representing a 13.8 fold elevation in precision. We showed by manual curation that drug-SE pairs that appeared in both data sources were highly enriched with true signals, many of which have not yet been included in FDA drug labels.

CONCLUSIONS

We have developed an efficient and effective drug safety signal ranking and strengthening approach We demonstrate that large-scale combining information from FAERS and biomedical literature can significantly contribute to drug safety surveillance.