Improve patient selection by analyzing unmet medical need under the existing standard of care and modeling inclusion/exclusion criteria to determine which specific subpopulations are most likely to respond to your therapy. 

Compare consistency of outcomes under a standard of care over time to inform potential opportunities for label expansion.

Monitor and benchmark patients throughout your trial to proactively determine which patients are the mostly likely to have an adverse event and mitigate and treat them proactively.

Medidata AI’s ground-breaking work in using historical clinical trial data to prevent CRS events in CAR-T trials has been accepted and presented at numerous industry conferences including ASCO, ASH and NCCN.

Model trial scenarios to create the optimal protocol and predict obstacles before they become trial failures—enhancing your ability to identify patients who are likely to respond to treatment, inform dosing strategy or proactively mitigate serious adverse events. This translates to safer trials and an increased likelihood of successful therapeutic outcomes.

See our recently published work in The Journal of the Alzheimer’s Association which analyzed and identified factors that are predictive of participant dropout using pooled data from historical randomized clinical trials. These factors can be used to improve clinical trial protocol design as well as to develop prevention strategies during future trials.

Identifying predictive factors of patient dropout in Alzheimer’s disease clinical trials

The Medidata Research Hub was created to work with key clinicians and researchers from academia, industry leaders, and leading data scientists to conduct cutting-edge research to improve the field’s understanding of therapies, most notably in immunotherapies such as CAR-T and bispecifics: 

Our historical clinical trial data is built upon over 20 years of approved and investigational products and can be leveraged to design and execute safer trials and advance scientific understanding. Our research topics commonly include: 

• Understanding  novel treatments that are associated with adverse events and benchmarks to mitigate or prevent catastrophic outcomes 
• Refining clinical guidelines to improve outcomes and safety profiles for patients
• Expanding access of these therapies to patients who need it the most

Medidata AI offers the largest available database of CAR-T and Bi-specific T-cell Engagers (BiTEs) therapies, made up of over 70 cross-sponsor trials for approved or investigational therapies. 

This data is not only used for commercial applications, including helping sponsors to design clinical trial protocols that help inform safety events or patient selection, but has been used by academics to show relationships between commonly collected lab data and the likelihood of adverse events. 

Our work has been widely featured in peer-reviewed journals, conferences and the press:

ASCO 2022 Predictors of severe CRS in longitudinal CAR T-cell clinical trial data 

ASH 2022 Predictors of severe CRS in longitudinal CAR T-cell clinical trial data

EHA 2023 Co-occurrence patterns of CRS and ICANS in patients undergoing autologous CD19-targeted CAR T-cell treatments

FierceBiotech Medidata links CAR-T’s cytokine release syndrome risk to common biomarkers in new study

CAR-T Cell Therapy Tanmay Jain speaks on CAR-T Cell Therapies and Medidata’s role in providing key insights.

CAR-T Trial Design Read this Whitepaper on enhancing CAR-T trial design with the Medidata AI CAR-T Data Cube.

Medidata has historical clinical trial data from 100,000+ patients that have been studied for MACE (Major Adverse Cardiovascular Events) such as stroke, myocardial infarction or heart attack, or heart failure. This includes patients who suffer from multiple comorbid conditions such as diabetes, obesity, hypertension, hyperlipidemia, and chronic kidney disease. Our data has a long follow-up time of up to six years, which is key to generating regulatory grade evidence. This dataset captures the most recent classes of drugs such as PCSK9, NOACs/DOACs, SGLT2, GLP1, and other standard-of-care therapies.

This data has been used in cutting-edge research including:

STH 2021 Comparing the Real-world and Clinical Trial Bleeding Rates Associated with Anticoagulation Treatment for Atrial Fibrillation

DDW 2022 Real-World Comparison of Healthcare Resource Utilization between Transoral Incisionless Fundoplication and Laparoscopic Nissen Fundoplication for the Treatment of Gastroesophageal Reflux Disease 

DDW 2022 Real-World Comparison of Characteristics of Patients who Received Transoral Incisionless Fundoplication vs. Laparoscopic Nissen Fundoplication for the Treatment of Gastroesophageal Reflux Disease

ISPOR 2023: Validation of Novel Identification Algorithms for Nonfatal Myocardial Infarction Using Uniform Objective Criteria and Pooled Clinical Trial Data

Medidata offers anonymized patient-level data to investigate and inform product development. We bring together the power of our historical clinical trial database with cutting-edge AI/ML techniques that preserves patient privacy, while offering opportunities for data sharing, cross collaboration, and innovation for these valuable, siloed data sources.

Neurips 2022: A Source Data Privacy Framework for Synthetic Clinical Trial Data

Neurips 2022: Synthetic Clinical Trial Data while Preserving Subject-Level Privacy

_synthesize 2023: Synthetic Data Revolutionizing Clinical Trial Data Collaboration and Research