薬物転用仮説生成の使用」RE：罰金薬「システム
Summary
Here we describe a protocol using the web-based drug repurposing hypothesis generation tool: “RE:fine Drugs.” This protocol can be modified to a user’s preferences at the level of the query type (gene, drug or disease) and/or the range of available advanced options.
Abstract
The promise of drug repurposing is that existing drugs may be used for new disease indications in order to curb the high costs and time for approval. The goal of computational methods for drug repurposing is to enable solutions for safer, cheaper and faster drug discovery. Towards this end, we developed a novel method that integrates genetic and clinical phenotype data from large-scale GWAS and PheWAS studies with detailed drug information on the concept of transitive Drug-Gene-Disease triads. We created “RE:fine Drugs,” a freely available, interactive dashboard that automates gene, disease and drug-based searches to identify drug repurposing candidates. This web-based tool supports a user-friendly interface that includes an array of advanced search and export options. Results can be prioritized in a variety of ways, including but not limited to, biomedical literature support, strength and statistical significance of GWAS and/or PheWAS associations, disease indications and molecular drug targets. Here we provide a protocol that illustrates the functionalities available in the “RE:fine Drugs” system and explores the different advanced options through a case study.
Introduction
高スループットの薬物およびリード化合物のスクリーニングを含む伝統的な創薬アプローチに関連したコストがかかり、非効率的なプロセスは、患者1,2のための治療法の研究の発見を翻訳するの遅れに貢献しています。 10億ドルと15年から20年の平均は、ベッドサイド3にベンチから新薬をもたらすために必要とされます。さらに、薬物の52％が第1相臨床試験で開発中に失敗し、フェーズ2に入る化合物の25％のみが完全な第3相臨床試験4に進みます。薬物転用または薬物の再配置の目標は、失敗した麻薬を更新および/またはより高速な患者に新しい治療法を提供し、高い成功率とするために、承認薬のための新規適応症を見つけることです。薬物再利用は3-12年5人の患者に使用するための薬を利用できるようにタイムラインを減少させることができます。薬物再利用のための重要な医療用途は次のとおりです。貧しいprognos伴う疾患をであり、低生存率、薬剤耐性疾患、積立不足病気の研究分野や貧困と不十分な患者集団。
計算薬物転用は、薬剤候補6のための新たな適応症のための仮説を生成することができる自動化されたワークフローを設計および検証するプロセスとして定義されています。既存の計算の薬物再利用方法は、標的ベース、知識ベース、シグネチャベース、ネットワークベースの分類、およびターゲットと機構系、遺伝子、疾患または薬物の視点から配向させることができるされています。また、計算のアプローチは、さらに、概念実証検証実験と再利用薬剤候補7のための小規模な臨床研究を加速することができます。我々は以前、「RE：罰金薬」で報告している、薬物-遺伝子-疾患関係8の推移理論に基づく薬物再利用仮説生成のために自由に利用できる、インタラクティブなWebベースのツール。全体的なグラムこの方法のOALは、体系的、臨床的、業界および規制社会など多様なコミュニティからのユーザーのための薬剤の再利用を可能にするために、薬物の多様な種類、遺伝学的および臨床データを統合することです。このシステムの基本的な方法は、以前にゲノムワイド関連解析（GWAS）と研究9,10を再利用する薬剤でphenomeワイド関連研究（PheWAS）データの使用が報告されています。これらのタイプのデータの新規な組み合わせは、他のターゲットに基づく方法6,11から当社のWebツールを区別します。
RE：罰金薬システムは、現在916薬、567の遺伝子と1770疾患をカバー60911の薬物転用仮説が含まれています。 Webツールは、研究者が対話的に、薬物転用仮説を検索し、多様な基準を使用してそれらに優先順位を付けるためのユーザーフレンドリーなインターフェイスを提供します。たとえば、ユーザーが生物医学文献および臨床試験がdatabaでのサポートを有する薬物転用仮説をフィルタリングすることができますSE、有意なp値、アソシエーションオッズ比または特定の指示によって。このシステムのための唯一の要件は、インターネットへのアクセスです。
Protocol
Representative Results
Discussion
The protocol described here for the RE:fine Drugs interactive dashboard can be modified in different ways according to the user’s preferences. This method uniquely integrates GWAS and PheWAS data as a novel paradigm underlying drug repurposing hypothesis generation. Specifically, this system provides access to both 52,966 PheWAS associations and 7,945 GWAS associations with advanced options to filter the results by the study type, effect size and/or significance level. Another advantage of this method over existing computational drug repurposing tools is that queries may be made from drug, gene or disease perspectives.
There are several limitations to this method. Currently, the PheWAS data is limited to primarily adult patient population from five institutions contained in the Electronic Medical Records and Genomics (eMERGE) network with a mean age of 69.5 years 12. Additionally, the “repurposing potential” feature uses co-occurrence of search terms in Medline abstracts as one of its criteria. It is well known that text mining methods using co-occurrence have limitations with respect to syntactical structure and literature bias. Thus, we recommend this feature be used as a starting point to explore the potential novelty and/or evidence supporting specific drug repurposing hypotheses and recommend additional investigation into the biomedical literature and clinical trial databases.
Future directions for this work not described here would be to extend this database to additional sources of GWAS and PheWAS data as they become available. Similar efforts to systematically translate results from large-scale GWAS studies into drug repurposing hypotheses have been previously published 9,13-14. It may be useful to compare these different workflows to predict drug candidates from GWAS data in future studies. Additionally, several other methods exist to computationally generate drug repurposing hypotheses from different data sources, including: genomics, transcriptomics, chemical structures, drug side effect profiles, as previously summarized 6,11. Future methodological advancements could also include automating drug combination predictions and providing information on drug toxicity to guide follow up studies for drug candidates.
Furthermore, the hypotheses generated from RE:fine Drugs may be further validated using electronic health records, before initiating clinical trials 15. Finally, future studies will be needed to compare this system to other target-based drug repurposing methods.
Disclosures
The authors have nothing to disclose.
Acknowledgements
This work was partially supported by the National Institutes of Health (NIH) Clinical and Translational Science Awards (CTSA) Grant (UL1TR001070) to the Ohio State University’s Center for Clinical and Translational Science (CCTS) and the National Library Of Medicine under Award Number T15LM011270.
Materials
| Access the homepage for “RE:fine Drugs” at the following link: http://drug-repurposing.nationwidechildrens.org. | n/a | n/a | The only requirement for this system is Internet access |
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