Identifying novel candidate compounds for therapeutic strategies in retinopathy of prematurity via computational drug-gene association analysis.

Purpose: Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. Although interventions such as anti-VEGF and laser have high success rates in treating severe ROP, current treatment and preventative strategies still have their limitations. Thus, we aim to identify drugs and chemicals for ROP with comprehensive safety profiles and tolerability using a computational bioinformatics approach. Methods: We generated a list of genes associated with ROP to date by querying PubMed Gene which draws from animal models, human studies, and genomic studies in the NCBI database. Gene enrichment analysis was performed on the ROP gene list with the ToppGene program which draws from multiple drug-gene interaction databases to predict compounds with significant associations to the ROP gene list. Compounds with significant toxicities or without known clinical indications were filtered out from the final drug list. Results: The NCBI query identified 47 ROP genes with pharmacologic annotations present in ToppGene. Enrichment analysis revealed multiple drugs and chemical compounds related to the ROP gene list. The top ten most significant compounds associated with ROP include ascorbic acid, simvastatin, acetylcysteine, niacin, castor oil, penicillamine, curcumin, losartan, capsaicin, and metformin. Antioxidants, NSAIDs, antihypertensives, and anti-diabetics are the most common top drug classes derived from this analysis, and many of these compounds have potential to be readily repurposed for ROP as new prevention and treatment strategies. Conclusion: This bioinformatics analysis creates an unbiased approach for drug discovery by identifying compounds associated to the known genes and pathways of ROP. While predictions from bioinformatic studies require preclinical/clinical studies to validate their results, this technique could certainly guide future investigations for pathologies like ROP.

Using Advanced Bioinformatics Tools to Identify Novel Therapeutic Candidates for Proliferative Vitreoretinopathy.

Purpose: Proliferative vitreoretinopathy (PVR) is the dreaded cause of failure following retinal detachment repair; however, no cures or preventative therapies exist to date. The purpose of this study was to use bioinformatics tools to identify drugs or compounds that interact with biomarkers and pathways involved in PVR pathogenesis that could be eligible for further testing for the prevention and treatment of PVR. Methods: We queried PubMed to compile a comprehensive list of genes described in PVR to date from human studies, animal models, and genomic studies found in the National Center for Biotechnology Information database. Gene enrichment analysis was performed using ToppGene on PVR-related genes against drug-gene interaction databases to construct a pharmacome and estimate the statistical significance of overrepresented compounds. Compounds with no clinical indications were filtered out from the resulting drug lists. Results: Our query identified 34 unique genes associated with PVR. Out of 77,146 candidate drugs or compounds in the drug databases, our analysis revealed multiple drugs and compounds that have significant interactions with genes involved in PVR, including antiproliferatives, corticosteroids, cardiovascular agents, antioxidants, statins, and micronutrients. Top compounds, including curcumin, statins, and cardiovascular agents such as carvedilol and enalapril, have well-established safety profiles and potentially could be readily repurposed for PVR. Other significant compounds such as prednisone and methotrexate have shown promising results in ongoing clinical trials for PVR. Conclusions: This bioinformatics approach of studying drug-gene interactions can identify drugs that may affect genes and pathways implicated in PVR. Predicted bioinformatics studies require further validation by preclinical or clinical studies; however, this unbiased approach could identify potential candidates among existing drugs and compounds that could be repurposed for PVR and guide future investigations. Translational Relevance: Novel repurposable drug therapies for PVR can be found using advanced bioinformatics models.

Using Advanced Bioinformatics Tools to Identify Novel Therapeutic Candidates for Age-Related Macular Degeneration.

Purpose: Age-related macular degeneration (AMD) is the most common cause of aging-related blindness in the developing world. Although medications can slow progressive wet AMD, currently, no drugs to treat dry-AMD are available. We use a systems or in silico biology analysis to identify chemicals and drugs approved by the Food and Drug Administration for other indications that can be used to treat and prevent AMD. Methods: We queried National Center for Biotechnology Information to identify genes associated with AMD, wet AMD, dry AMD, intermediate AMD, and geographic atrophy to date. We combined genes from various AMD subtypes to reflect distinct stages of disease. Enrichment analysis using the ToppGene platform predicted molecules that can influence AMD genes. Compounds without clinical indications or with deleterious effects were manually filtered. Results: We identified several drug/chemical classes that can affect multiple genes involved in AMD. The drugs predicted from this analysis include antidiabetics, lipid-lowering agents, and antioxidants, which could theoretically be repurposed for AMD. Metformin was identified as the drug with the strongest association with wet AMD genes and is among the top candidates in all dry AMD subtypes. Curcumin, statins, and antioxidants are also among the top drugs correlating with AMD-risk genes. Conclusions: We use a systematic computational process to discover potential therapeutic targets for AMD. Our systematic and unbiased approach can be used to guide targeted preclinical/clinical studies for AMD and other ocular diseases. Translational Relevance: Advanced bioinformatics models identify novel chemicals and approved drug candidates that can be efficacious for different subtypes of AMD.

Lynx: a knowledge base and an analytical workbench for integrative medicine.

Lynx (http://lynx.ci.uchicago.edu) is a web-based database and a knowledge extraction engine. It supports annotation and analysis of high-throughput experimental data and generation of weighted hypotheses regarding genes and molecular mechanisms contributing to human phenotypes or conditions of interest. Since the last release, the Lynx knowledge base (LynxKB) has been periodically updated with the latest versions of the existing databases and supplemented with additional information from public databases. These additions have enriched the data annotations provided by Lynx and improved the performance of Lynx analytical tools. Moreover, the Lynx analytical workbench has been supplemented with new tools for reconstruction of co-expression networks and feature-and-network-based prioritization of genetic factors and molecular mechanisms. These developments facilitate the extraction of meaningful knowledge from experimental data and LynxKB. The Service Oriented Architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

An integrative computational approach for prioritization of genomic variants.

An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.

Lynx: a database and knowledge extraction engine for integrative medicine.

We have developed Lynx (http://lynx.ci.uchicago.edu)--a web-based database and a knowledge extraction engine, supporting annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Its underlying knowledge base (LynxKB) integrates various classes of information from >35 public databases and private collections, as well as manually curated data from our group and collaborators. Lynx provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization to assist the user in extracting meaningful knowledge from LynxKB and experimental data, whereas its service-oriented architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.