我们有 12 个免疫学数据库，包括最新的针对新冠肺炎的数据库。
获得更敏锐的洞察力
在化合物的开发过程中，您必须做出关键的决定，以确保药物获得批准并在市场上成功。Success depends not only on the drug’s performance, but also on providing evidence of value compared to the competition.
There is wealth of published clinical trial outcomes data, and it can be leveraged for decsison making across the drug development life cycle. But, doingthis requires a massive investment of time and resources into mining structured data that is analysis ready.
Certara has created an extensive collection of over 55 Clinical Trial Outcomes Databases covering a wide range of therapeutic areas that capture high-quality public source data on efficacy and safety, drug, disease and trial characteristics, trial design, and the competitive landscape to inform critical decisions.
转到 CODEx 平台可进行分析的临床试验结果数据
Certara’s Clinical Outcomes Databases include information for marketed drugs as well as drugs in development; data sources include journal publications, conference posters, regulatory reviews, regulatory clinical trial websites (e.g., clinicaltrials.gov), etc.
他们利用宝贵的公共临床试验数据为以下方面提供关键的药物开发见解：
- 比较疗效/安全性
- Influential population and trial characteristics
- Biomarker-outcome relationships · Trial design optimization
- 商业可行性
可视化、探索、分析和交流
CODEx, our intuitive, interactive, web-based graphical interface unlocks the value and richness of public and proprietary clinical outcome data. This interface allows you to easily visualize, explore, analyze, and communicate answers to critical R&D questions to a broad audience.
- 使用内置过滤器轻松选择数据子集
- 查看交互式表格和图形以进行荟萃分析
- 及时访问数据库更新
- 根据您的需求获取自定义数据库内容
A.I.-Powered Analytics Across the Clinical Trial Landscape
Artificial intelligence (AI) extends the capabilities of CODEx clinical trial exploration platform. Featuring a suite of powerful deep learning AI models, CODEx enables users to quickly search, identify and extract insights from both internal and external content sources that relate to their clinical outcome database. Clinical trial investigators can now draw better conclusions around therapeutic effectiveness and safety, anticipating clinical trial outcomes and planning new studies.
- Quickly identify insights across structured and unstructured data types such as published research, CSRs, scanned documents and press releases
- Use natural language processing to query and extract relevant data points from unstructured literature to expand clinical outcome datasetsConsume external public data and integrate data from internal sources – all in one platform
- No code analytics tool for turning unstructured content into structured datasets
More than 55 indications are supported
Cardiovascular and metabolic disorders:房颤预防、慢性肾脏病、心力衰竭、NASH、肥胖症、继发性中风预防、T1DM 和 T2DM……
免疫学： Crohn’s Disease, multiple sclerosis, psoriasis, rheumatoid arthritis, lupus, ulcerative colitis, atopic dermatitis, …
肿瘤学： Metastatic breast cancer, multiple myeloma, non-small cell lung cancer, Non-Hodgkin’s lymphoma, small cell lung cancer, PD-(L)1 treatment of solid tumors…
中枢神经系统、疼痛及其他： Alzheimer’s, AMD, asthma, chronic pain, COVID-19, cystic fibrosis, glaucoma, HBV, HCV, idiopathic pulmonary fibrosis, major depressive disorder, migraine, narcolepsy, osteoporosis, schizophrenia…
Jaap 的主要经验在于应用建模和模拟来优化治疗策略、试验设计和药物开发决策。他是基于模型的荟萃分析方面的全球领先专家，发表了广泛的著作，并因其学术贡献获得了多个奖项。
在 Certara，Matthew Zierhut 博士通过基于模型的元分析 (MBMA)，推进将外部聚合临床试验数据整合到开发决策和商业和监管策略中。Matt 与临床开发团队密切合作，确保在做出最关键决策的时候，能利用 MBMA 发挥最佳影响力。
Jaap 的主要经验在于应用建模和模拟来优化治疗策略、试验设计和药物开发决策。他是基于模型的荟萃分析方面的全球领先专家，发表了广泛的著作，并因其学术贡献获得了多个奖项。
在 Certara，Matthew Zierhut 博士通过基于模型的元分析 (MBMA)，推进将外部聚合临床试验数据整合到开发决策和商业和监管策略中。Matt 与临床开发团队密切合作，确保在做出最关键决策的时候，能利用 MBMA 发挥最佳影响力。
