is to help a pharmaceutical company to stay GCP compliant, improve patient safety, make a clinical study more predictable, and optimize monitoring resources. Prevent negative surprises.
trial management system that is more predictive than retrospective. It integrates the knowledge from previous trials with contextual, real-world data.
to simplify safety and regulatory aspects of clinical trials, making them transparent and understandable, allowing to focus on medical and organizational aspects, run trials with greater confidence and performance.
Creation of a fully automatized, centralized, best in the class risk management cloud platform for improving patient safety, data integrity, and trial transparency.
Our vision is based on three main pillars:
- control of risks, performance and data quality
- application of the latest data mining technologies to gain new knowledge about the data
- follow up on the identified risks until resolution and development of a risk-based strategy for a focused use of resources available for a trial
Cyntegrity started in 2013, when Artem Andrianov and Martin Koch, former employees of ERT, created the first prototypes of a solution for the clinical data analysis. The need for an elegant, well-architected risk-management system for clinical research was clear even then. Today, Cyntegrity is the platform of choice for many global pharma enterprises and clinical research organizations.
Cyntegrity’s solution has evolved in progressive ways over time. Our team has grown with skilled, enthusiastic engineers and scientists. Cyntegrity opened the opportunity to everyone to optimize the clinical operations with the GCP-driven workflow, predictive analytics, and intelligence of the specialized algorithms. People with limited statistical and mathematical experience can use it right away and more math-savvy folks can customize it for their needs.
- only a standardized data care approach can guarantee valid scientific evidence for approval
- people’s trust in approved medicine is the foundation for new drug discovery
- any medicine that we use for ourselves or our relatives must be clinically proven in the most genuine scientific way
- by using a combination of medical knowledge and statistical methods, we can identify anomalies in clinical data and minimize potential risks