Design and application of a knowledge network for automatic prioritization of drug mechanisms.

MOTIVATION: Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug's mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications. RESULTS: In this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in nature and found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNet's ability to use mechanistic insight to identify a drug's mechanistic target, with a mean reciprocal rank of 0.525 on a test set of known drug-target interactions. Finally, we walked through repurposing examples of the anti-cancer drug imatinib for use in the treatment of asthma, and metolazone for use in the treatment of osteoporosis, to demonstrate this method's utility in providing mechanistic insight into repurposing predictions it provides. AVAILABILITY AND IMPLEMENTATION: The Python code to reproduce the entirety of this analysis is available at: https://github.com/SuLab/MechRepoNet (archived at https://doi.org/10.5281/zenodo.6456335). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Topical Review: Contact Lens Eye Health and Safety Considerations in Government Policy Development.

SIGNIFICANCE: As new federal or state policies are introduced in the United States to shape the evolving contact lens market, it has never been more important to amplify the importance of patient health and safety during contact lens wear and promote the value of the eye care professional-patient relationship.Within the United States, contact lenses are regulated by the Food and Drug Administration as class II or III medical devices that require additional regulatory and professional oversight to keep consumers safe. The contact lens market and broader eye health landscape are rapidly changing. Recently, the U.S. Federal Trade Commission finalized its 10-year review of the Contact Lens Rule, implementing new policies that will shape the contact lens market in the United States for years to come. The purpose of this clinical perspective was to compile and review key data regarding contact lens-related adverse events, including their economic impact on the health care system, to inform government policy development. Although contact lenses provide many benefits to the wearer, a variety of complications can occur ranging from asymptomatic events or mild discomfort to severe sight-threatening adverse events such as microbial keratitis. Patients who do not routinely visit their eye care professional or do not receive the lenses prescribed to them are at a greater risk of contact lens-related adverse events. Nearly 1 million people in the United States experience ocular infections or inflammation annually, resulting in significant health care costs. The economic burden of contact lens-related microbial keratitis in the United States has been estimated to be approximately $175 million annually. The importance of eye care professional oversight of contact lens wear cannot be emphasized enough to key stakeholders, including lawmakers, government regulators, contact lens manufacturers and distributors, and the broader eye health community.

Applying citizen science to gene, drug and disease relationship extraction from biomedical abstracts.

MOTIVATION: Biomedical literature is growing at a rate that outpaces our ability to harness the knowledge contained therein. To mine valuable inferences from the large volume of literature, many researchers use information extraction algorithms to harvest information in biomedical texts. Information extraction is usually accomplished via a combination of manual expert curation and computational methods. Advances in computational methods usually depend on the time-consuming generation of gold standards by a limited number of expert curators. Citizen science is public participation in scientific research. We previously found that citizen scientists are willing and capable of performing named entity recognition of disease mentions in biomedical abstracts, but did not know if this was true with relationship extraction (RE). RESULTS: In this article, we introduce the Relationship Extraction Module of the web-based application Mark2Cure (M2C) and demonstrate that citizen scientists can perform RE. We confirm the importance of accurate named entity recognition on user performance of RE and identify design issues that impacted data quality. We find that the data generated by citizen scientists can be used to identify relationship types not currently available in the M2C Relationship Extraction Module. We compare the citizen science-generated data with algorithm-mined data and identify ways in which the two approaches may complement one another. We also discuss opportunities for future improvement of this system, as well as the potential synergies between citizen science, manual biocuration and natural language processing. AVAILABILITY AND IMPLEMENTATION: Mark2Cure platform: https://mark2cure.org; Mark2Cure source code: https://github.com/sulab/mark2cure; and data and analysis code for this article: https://github.com/gtsueng/M2C_rel_nb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Time-resolved evaluation of compound repositioning predictions on a text-mined knowledge network.

BACKGROUND: Computational compound repositioning has the potential for identifying new uses for existing drugs, and new algorithms and data source aggregation strategies provide ever-improving results via in silico metrics. However, even with these advances, the number of compounds successfully repositioned via computational screening remains low. New strategies for algorithm evaluation that more accurately reflect the repositioning potential of a compound could provide a better target for future optimizations. RESULTS: Using a text-mined database, we applied a previously described network-based computational repositioning algorithm, yielding strong results via cross-validation, averaging 0.95 AUROC on test-set indications. However, to better approximate a real-world scenario, we built a time-resolved evaluation framework. At various time points, we built networks corresponding to prior knowledge for use as a training set, and then predicted on a test set comprised of indications that were subsequently described. This framework showed a marked reduction in performance, peaking in performance metrics with the 1985 network at an AUROC of .797. Examining performance reductions due to removal of specific types of relationships highlighted the importance of drug-drug and disease-disease similarity metrics. Using data from future timepoints, we demonstrate that further acquisition of these kinds of data may help improve computational results. CONCLUSIONS: Evaluating a repositioning algorithm using indications unknown to input network better tunes its ability to find emerging drug indications, rather than finding those which have been randomly withheld. Focusing efforts on improving algorithmic performance in a time-resolved paradigm may further improve computational repositioning predictions.

Quantitative Metaproteomics and Activity-Based Probe Enrichment Reveals Significant Alterations in Protein Expression from a Mouse Model of Inflammatory Bowel Disease.

Tandem mass spectrometry based shotgun proteomics of distal gut microbiomes is exceedingly difficult due to the inherent complexity and taxonomic diversity of the samples. We introduce two new methodologies to improve metaproteomic studies of microbiome samples. These methods include the stable isotope labeling in mammals to permit protein quantitation across two mouse cohorts as well as the application of activity-based probes to enrich and analyze both host and microbial proteins with specific functionalities. We used these technologies to study the microbiota from the adoptive T cell transfer mouse model of inflammatory bowel disease (IBD) and compare these samples to an isogenic control, thereby limiting genetic and environmental variables that influence microbiome composition. The data generated highlight quantitative alterations in both host and microbial proteins due to intestinal inflammation and corroborates the observed phylogenetic changes in bacteria that accompany IBD in humans and mouse models. The combination of isotope labeling with shotgun proteomics resulted in the total identification of 4434 protein clusters expressed in the microbial proteomic environment, 276 of which demonstrated differential abundance between control and IBD mice. Notably, application of a novel cysteine-reactive probe uncovered several microbial proteases and hydrolases overrepresented in the IBD mice. Implementation of these methods demonstrated that substantial insights into the identity and dysregulation of host and microbial proteins altered in IBD can be accomplished and can be used in the interrogation of other microbiome-related diseases.

Development and validation of LC-MS/MS method for quantification of protease inhibitor Pepstatin A to monitor its robust clearance in vaccine downstream process.

Pepstatin A reversibly inhibits aspartic acid proteases and minimizes the impact of protease-induced degradation in recombinant protein manufacturing process. Pepstatin A is considered as a process-related impurity and must be characterized and controlled during manufacturing. Herein we describe the development and validation of an LC-MS/MS method for the quantitation of pepstatin A to monitor its robust clearance in vaccine purification process. Analyte extraction from process intermediates was carried out using 10% acetonitrile/water extraction method. Acetyl-pepstatin was used as internal standard (IS). Pepstatin A and IS were resolved on a C18 column using 10 mM ammonium acetate in water and methanol/acetonitrile mobile phase system. A triple quadrupole mass spectrometer operating in the positive electrospray ionization mode with multiple reaction monitoring was used to detect Pepstatin A and IS transitions of m/z 686.5 to 229.3 and 644.5 to 229.3, respectively. The method was validated for specificity, linearity, accuracy, repeatability (precision), intermediate precision, and assay robustness. The assay was linear over the range of calibration standards 0.5-100 ng/mL. The Lower-limit-of-quantification (LLOQ) of the method was 0.50 ng/mL.

DrugMechDB: A Curated Database of Drug Mechanisms.

Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to a disease prediction. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repositioning models or as a valuable resource for training such models.

DrugMechDB: A Curated Database of Drug Mechanisms.

Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to disease predictions. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repurposing models or as a valuable resource for training such models.

Structured reviews for data and knowledge-driven research.

Hypothesis generation is a critical step in research and a cornerstone in the rare disease field. Research is most efficient when those hypotheses are based on the entirety of knowledge known to date. Systematic review articles are commonly used in biomedicine to summarize existing knowledge and contextualize experimental data. But the information contained within review articles is typically only expressed as free-text, which is difficult to use computationally. Researchers struggle to navigate, collect and remix prior knowledge as it is scattered in several silos without seamless integration and access. This lack of a structured information framework hinders research by both experimental and computational scientists. To better organize knowledge and data, we built a structured review article that is specifically focused on NGLY1 Deficiency, an ultra-rare genetic disease first reported in 2012. We represented this structured review as a knowledge graph and then stored this knowledge graph in a Neo4j database to simplify dissemination, querying and visualization of the network. Relative to free-text, this structured review better promotes the principles of findability, accessibility, interoperability and reusability (FAIR). In collaboration with domain experts in NGLY1 Deficiency, we demonstrate how this resource can improve the efficiency and comprehensiveness of hypothesis generation. We also developed a read-write interface that allows domain experts to contribute FAIR structured knowledge to this community resource. In contrast to traditional free-text review articles, this structured review exists as a living knowledge graph that is curated by humans and accessible to computational analyses. Finally, we have generalized this workflow into modular and repurposable components that can be applied to other domain areas. This NGLY1 Deficiency-focused network is publicly available at http://ngly1graph.org/. AVAILABILITY AND IMPLEMENTATION: Database URL: http://ngly1graph.org/. Network data files are at: https://github.com/SuLab/ngly1-graph and source code at: https://github.com/SuLab/bioknowledge-reviewer. CONTACT: asu@scripps.edu.

Wikidata as a knowledge graph for the life sciences.

Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.

Improving your spectacle patients' in-practice experience with contact lenses during frame selection.

PURPOSE: A market research survey was conducted to assess the impact of offering complimentary contact lenses (CLs) to spectacle-only wearing patients during frame selection with regards to their in-office experience, the transaction amount for their eyewear purchase and the likelihood of proceeding with a comprehensive contact lens fitting. METHODS: Five optometry offices in the US participated. An initial interviewing phase served as a control during which optometrists treated spectacle-only wearing patients in the usual manner for frame selection. After this, the offices transitioned into a test phase where patients were offered the opportunity to wear CLs while selecting new spectacle frames. Only patients 18 or older who had not expressed an interest in CLs were invited to participate. A brief survey was completed on an iPad following the visit by all patients in both control and test phases. RESULTS: 410 patients (205 test, 205 control) participated. 63% of the test group elected to wear CLs (40% spherical, 20% toric, 35% multifocal, 5% monovision). Patients wearing CLs spent more on their eyewear purchase ($708 vs $593, p = 0.04), were greater than 2.5X more likely to have received or scheduled a CL fit (p = 0.01), and were greater than 3X more likely to consider scheduling a CL fitting in the future (p = 0.0003). Additionally, 93% reported that they were highly satisfied with the experience and 86% said they would wear CLs to select frames again (86%). CONCLUSIONS: Offering CLs to spectacle-only patients positively impacts eyewear selection and purchase and can grow the overall CL business.

Anxiety sensitivity: stability in prospective research.

Several recent panic prevention studies suggest that anxiety sensitivity, as measured by the Anxiety Sensitivity Index (ASI), may not be stable under certain conditions. In two investigations [Behav. Ther. 32 (2001) 725; Disertation Abstr. Int. 62 (2001) 4226], wait-list or no-treatment conditions produced ASI scores at follow-up that were significantly reduced from baseline and comparable to those of the intervention groups. Although design characteristics could not rule out regression to the mean as the source of these changes, the authors suggested that these findings were most likely due to nonspecific factors such as reassurance, support, or the expectation of receiving subsequent treatment. The present study sought to replicate and extend these findings by analyzing the contribution of a detailed diagnostic assessment on ASI scores. Two cohorts of high-risk-for-panic participants scoring in the high range of the ASI were studied. Cohort 1 received a detailed diagnostic assessment and then either no-treatment or one of two anxiety sensitivity reduction interventions. Cohort 2 did not receive a detailed diagnostic assessment or an intervention. Both groups were followed up 2 weeks after baseline assessment. Results were consistent with the hypothesis that ASI total and subscale scores are unstable in the presence of structured interviews. Participants receiving a diagnostic assessment produced ASI scores that were significantly lower than at baseline with the average ASI score dropping from the high to the average range. ASI scores of participants not receiving a diagnostic assessment, however, were unchanged from baseline.

Virtual reality exposure therapy for the treatment of fear of flying: a controlled investigation.

Forty-five participants who refused to fly during a screening test and who also met Diagnostic and Statistical Manual of Mental Disorders criteria for specific phobia, agoraphobia, or panic disorder with agoraphobia were randomly assigned to 5 sessions of either virtual reality exposure (VRE) or attention-placebo group treatment (GT). At posttreatment, 65% of VRE participants and 57% of GT participants flew during a test flight. Both groups showed significant improvement following treatment on standardized self-report measures of flight anxiety, with a better outcome for the VRE group on 4 of 5 of these measures. At 6-month follow-up, however, most group differences had disappeared; VRE resulted in a better outcome on only 1 of 5 standardized flight anxiety measures.