Clinical Trials & Rare Disease

Delve into meticulously curated and interconnected clinical trial records, trial metadata, and their relationships to indications and drugs.

For trials conducted on orphan designations, this package also provides insights into known targets, along with standardized trial termination reasons.

The package contains:

- An in-depth list of 4,815 rare diseases

- 154,800+ clinical trials and mapped conditions

- 3,788 biological entities mentioned in rare disease trials

The data is meticulously organized, curated, and optimized for traditional and machine-learning applications to:

- **Identify areas of opportunity** by evaluating trial trends by disease

- **Evaluate competitors'** clinical strategies, including inclusion and exclusion criteria

- **Validate feasibility** by analyzing trends in rare disease trial failures and outcomes

DrugBank is the intelligence operating system (OS) built for drug discovery and development. Our mission is to ensure biomedical data and commercial insight is connected and used to its fullest potential, empowering biopharma to bring life-changing therapies to patients faster. Using both AI and expert curation, we’ve crafted the most trusted, comprehensive drug knowledgebase to draw fast, defensible, and contextualized insights. We offer AI and machine learning-ready datasets for download that are packaged to serve your unique needs.

The following data modules are included in this package:

- **Clinical Trials**: Provides detailed information about clinical trials including interventions, trial arms, location, sponsor, trial conditions and more. Information is normalized, linked, and many relevant metadata descriptions are structured and ready for analysis.

- **Conditions**: This module provides a complete hierarchy of condition terms describing diseases, symptoms, and other medical conditions.

- **Drugs**: This module provides a comprehensive list of all approved and investigational drugs. Each drug includes detailed molecular descriptions and relevant nomenclature and identifiers. The dataset includes coverage of small molecule drugs and biologics.

- **Drug Protein Relationships and Drug Targets**: This module describes the relationship between drugs and targets, enzymes, carriers, or transporters. Annotation includes the pharmacological action, and the type of interaction (antagonist, agonist, substrate, inhibitor, or inducer).

- **Rare Diseases**: This module describes the status of drugs intended for use against rare diseases. The dataset offers a view of the orphan designations without being specifically tied to any one set of regulatory standards or rules.

- **References**: This module provides comprehensive citations, substantiating the facts mentioned within DrugBank

Workflow:

- Click the “Request” button
- DrugBank will get in touch with you to describe how the data will be shared

本数据集涵盖经过精心整理且相互关联的临床试验记录、试验元数据，以及它们与适应症和药物之间的关联关系。
针对带有孤儿药资格认定（Orphan Designations）的试验，本数据包还提供了已知靶点的相关信息，以及标准化的试验终止原因分析。

本数据包包含以下内容：
- 一份涵盖4815种罕见病的详尽列表
- 超过154800项临床试验及映射后的病症条目
- 罕见病临床试验中提及的3788个生物实体

本数据集经过精心组织、整理与优化，可适配传统应用与机器学习场景，用于：
- **挖掘潜在研发机遇**：按疾病维度分析临床试验趋势
- **评估竞品临床策略**：涵盖入组与排除标准分析
- **验证研发可行性**：通过分析罕见病临床试验失败与结局的趋势

DrugBank是为药物研发打造的智能操作系统（Intelligence Operating System, OS）。我们的使命是实现生物医学数据与商业洞察的互联互通并充分挖掘其价值，助力生物制药企业更快地将改变患者命运的治疗方案推向市场。我们结合人工智能（Artificial Intelligence, AI）与专家人工编目，打造了值得信赖的综合性药物知识库，可快速产出严谨且贴合场景的洞察结果。我们提供可直接用于人工智能与机器学习的数据集供下载，所有数据包均针对您的个性化需求进行定制封装。

本数据包包含以下数据模块：
- **临床试验模块**：提供临床试验的详细信息，涵盖干预措施、试验分组、开展地点、申办方、试验适应症等多项内容。所有信息均经过归一化处理并建立关联，多数相关元数据描述已结构化，可直接用于分析。
- **病症模块**：提供完整的病症术语层级体系，涵盖疾病、症状及其他医学病症相关术语。
- **药物模块**：收录所有已获批及在研药物的完整列表，每种药物均包含详细的分子描述、相关命名与标识符。本数据集覆盖小分子药物与生物制品。
- **药物-蛋白质关联与药物靶点模块**：阐述药物与靶点、酶、载体或转运蛋白之间的关联关系，注释内容涵盖药理学作用及相互作用类型（拮抗剂、激动剂、底物、抑制剂或诱导剂）。
- **罕见病模块**：介绍针对罕见病的药物研发状态，本数据集提供孤儿药资格认定的相关视图，且不局限于某一套特定的监管标准或规则。
- **参考文献模块**：提供全面的引用文献，用于佐证DrugBank知识库中提及的各项事实依据。

使用流程：
- 点击「申请」按钮
- DrugBank将与您联系，告知数据的共享方式

AI搜集汇总

背景与挑战

背景概述

该数据集是一个专注于罕见疾病和临床试验的综合性数据包，包含4,815种罕见疾病列表、超过154,800个临床试验及其映射条件，以及3,788个在罕见疾病试验中提到的生物实体。数据经过精心组织和优化，适用于传统和机器学习应用，旨在帮助用户通过分析试验趋势识别机会、评估竞争对手策略，并验证临床试验的可行性。数据集还提供多个结构化模块，如临床试验详情、疾病条件、药物信息等，以支持深入分析。